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"abstract": "Adaptive hypermedia learning systems aim to improve the usability of hypermedia by personalizing domain knowledge to the students? needs (represented by the student model). This study investigates student modeling via machine-learning techniques. Two techniques are applied and compared to provide meaningful analysis and class labels of the student clusters. The first technique is clustering of the student data set using principal component analysis. The second technique involves two-levels of clustering: the self-organizing map at the first level and principal component analysis at the second level. Cluster analysis via these two techniques will determine the number of clusters, the class labels based on the degree of variance and eigenvectors, which can represent the knowledge states of each cluster or group of students. It is found that implementing the self-organizing map as a preprocessor to principal component analysis improves the quality of cluster analysis. Findings are supported by experimental results.",
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"fullName": "Yashwant Prasad Singh",
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"abstract": "Augmented Reality (AR) is a technology that allows overlaying the computer-generated virtual information into the user’s view of the real world, and with changes of his position and line of sight, the virtual objects will change accordingly. AR has many potential applications in industrial operations and academic research. First, this paper discusses the development of 3D visualization of crystal molecular structure. Next, the paper addresses the design of 3D macromolecule visualization based on AR. Then, the paper discusses 3D modeling of macromolecule. 3D model of crystal molecular is built with grid modeling in OpenGL. Finally, the paper makes a verifying research on it. The data of molecular was downloaded from internet database. The AR visualization system is established in ARToolKit. The result is stable, immersive and interactive.",
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"abstract": "Demand estimation in public transport is critical for transport stakeholders. Thanks to the emerging technologies in recent years, many sources of mobility data are available to model passengers flow in public transport network. One of the most added-value mobility data is smart card Origin-Destination (OD) data. These data could inform us on when, where and how flows transit within the network. The OD matrix used in this work is obtained from smart card data collected by Automated Fare Collection (AFC) system in the Greater Paris Area which is called Navigo Pass. Despite its immense value, this matrix doesn't cover the entire passenger flow. This is due to fraud, other types of tickets (e.g. the standard paper ticket) and uncertainties in destination estimates. In this paper we propose a two-step approach for correcting and scaling smart card OD matrix based on adapted Growth Factor model considering the complexity caused by temporal variation of the OD matrix. In the first step we map all the OD pairs in the OD matrix over our area of study to infer their departure and arrival stations and time. In the second step we exploit passengers' counting data and use growth factor model to scale the OD matrix to obtain a new corrected matrix which can present the real flow in the transit network. We apply our proposed methodology to scale an OD matrix constructed only from smart card validation data which presents between 40% to 65% of the overall flow. For this purpose, passengers' counting data are exploited.",
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"abstract": "To the trained-eye, experts can often identify a team based on their unique style of play due to their movement, passing and interactions. In this paper, we present a method which can accurately determine the identity of a team from spatiotemporal player tracking data. We do this by utilizing a formation descriptor which is found by minimizing the entropy of role-specific occupancy maps. We show how our approach is significantly better at identifying different teams compared to standard measures (i.e., Shots, passes etc.). We demonstrate the utility of our approach using an entire season of Prozone player tracking data from a top-tier professional soccer league.",
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"abstract": "With the most professional leagues, professional players, and national competitions, soccer is perhaps the most popular sport in the world. One of the reasons why soccer is so popular is because it is simple: two teams trying to score in each other’s goal only using their feets and heads. However, when we go deeper inside, there are numerous factors that can subtly or significantly impact the entire result of the game. The factor that this paper focuses on is the ball distribution between each player. It is common sense that teams with equal contribution from every player indicate better teamwork, and thus they are more likely to be stronger than teams that rely on one or two key players. In order to qualify this idea and devise useful strategies accordingly, we employed Markov chain and Principal Component Analysis (PCA). Through the Markov chain, we modeled a soccer game into a system (team) of eleven sections (players) continuously transitioning (giving passes) to other sections until they score a goal. Through PCA, we compared the patterns we found from the Markov chain modeling to other soccer statistics to evaluate how related the pattern we found is to the victory of the soccer team compared to other well-known soccer statistics. The evaluation of our approach shows promising results in analyzing soccer and constructing the most ideal player formation to win the game.",
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"content": "This paper presents a method for text line segmentation of challenging historical manuscript images. These manuscript images contain narrow interline spaces with touching components, interpenetrating vowel signs and inconsistent font types and sizes. In addition, they contain curved, multi-skewed and multi-directed side note lines within a complex page layout. Therefore, bounding polygon labeling would be very difficult and time consuming. Instead we rely on line masks that connect the components on the same text line. Then these line masks are predicted using a Fully Convolutional Network (FCN). In the literature, FCN has been successfully used for text line segmentation of regular handwritten document images. The present paper shows that FCN is useful with challenging manuscript images as well. Using a new evaluation metric that is sensitive to over segmentation as well as under segmentation, testing results on a publicly available challenging handwritten dataset are comparable with the results of a previous work on the same dataset.",
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"abstract": "Most autonomous vehicles rely on some kind of map for localization or navigation. Outdated maps however are a risk to the performance of any map-based localization system applied in autonomous vehicles. It is necessary to update the used maps to ensure stable and long-term operation. We address the problem of computing landmark updates live in the vehicle, which requires efficient use of the computational resources. In particular, we employ a graph-based sliding window approach for simultaneous localization and incremental map refinement. We propose a novel method that approximates sliding window marginalization without inducing fill-in. Our method maintains the exact same sparsity pattern as without performing marginalization, but simultaneously improves the landmark estimates. The main novelty of this work is the derivation of sparse global priors that approximate dense marginalization. In comparison to state-of-the-art work, our approach utilizes global instead of local linearization points, but still minimizes linearization errors. We first approximate marginalization via Kullback-Leibler divergence and then recalculate the mean to compensate linearization errors. We evaluate our approach on simulated and real data from a prototype vehicle and compare our approach to state-of-the-art sliding window marginalization.",
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"content": "Most autonomous vehicles rely on some kind of map for localization or navigation. Outdated maps however are a risk to the performance of any map-based localization system applied in autonomous vehicles. It is necessary to update the used maps to ensure stable and long-term operation. We address the problem of computing landmark updates live in the vehicle, which requires efficient use of the computational resources. In particular, we employ a graph-based sliding window approach for simultaneous localization and incremental map refinement. We propose a novel method that approximates sliding window marginalization without inducing fill-in. Our method maintains the exact same sparsity pattern as without performing marginalization, but simultaneously improves the landmark estimates. The main novelty of this work is the derivation of sparse global priors that approximate dense marginalization. In comparison to state-of-the-art work, our approach utilizes global instead of local linearization points, but still minimizes linearization errors. We first approximate marginalization via Kullback-Leibler divergence and then recalculate the mean to compensate linearization errors. We evaluate our approach on simulated and real data from a prototype vehicle and compare our approach to state-of-the-art sliding window marginalization.",
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"title": "Study of the Evolution of Online User Interest Behavior",
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"abstract": "The click behavior of online users can be regarded as a kind of movement in virtual space, which is usually driven by human interests. Though many breakthroughs have been made in exploring the dynamics of human behavior, little is known about the dynamic evolution of human interest in virtual space, partly due to the extreme difficulty in getting enough behavioral data and accessing the human mind from observations. Based on the large-scale online user behavior data obtained by China Internet Network Information Center, this paper quantifies the browsing behavior of online users from the aspects of moving step length, dwell time and return step length. By constructing the maximum entropy model, combined with the memory effect of interest, the reasons for the long-tailed distribution are explained respectively. Based on the analysis results, we propose an interest stickiness measurement algorithm for efficiently evaluating the attractiveness of web interest points to users. The regularity and predictability of group users' browsing interest points play a significant role in science, engineering, business promotion, development of the economy of attention and other specific fields.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The click behavior of online users can be regarded as a kind of movement in virtual space, which is usually driven by human interests. Though many breakthroughs have been made in exploring the dynamics of human behavior, little is known about the dynamic evolution of human interest in virtual space, partly due to the extreme difficulty in getting enough behavioral data and accessing the human mind from observations. Based on the large-scale online user behavior data obtained by China Internet Network Information Center, this paper quantifies the browsing behavior of online users from the aspects of moving step length, dwell time and return step length. By constructing the maximum entropy model, combined with the memory effect of interest, the reasons for the long-tailed distribution are explained respectively. Based on the analysis results, we propose an interest stickiness measurement algorithm for efficiently evaluating the attractiveness of web interest points to users. The regularity and predictability of group users' browsing interest points play a significant role in science, engineering, business promotion, development of the economy of attention and other specific fields.",
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"authors": [
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"affiliation": "Northwest Normal University",
"fullName": "Yong Li",
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"affiliation": "Northwest Normal University",
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"affiliation": "China Internet Network Information Center",
"fullName": "Changqing Wang",
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"abstract": "Because tests at Army Test and Evaluation Command (ATEC) ranges can often only be conducted under specific atmospheric conditions, climatologies of each range could be a very useful tool for long-range test planning. Such datasets can provide the probability density function of near-surface wind speed, percent cloud cover, temperature, precipitation, turbulence intensity, upper-atmospheric wind speed, etc., as a function of the month of the year, time of day, and location on the range. The STAR Institute with guidance from the Ohio Supercomputer Center is working on porting the Climate Four Dimensional Data Assimilation (CFDDA) technology developed by the National Center for Atmospheric Research to version 3 of the Weather and Research Forecast (WRF) model for use on systems within the Department of Defense High Performance Computing Modernization Prograf m. The fully parallelized WRF-CFDDA system uses data-assimilation by Newtonian relaxation, to generate a regional reanalysis that is consistent with both observations and model dynamics for every hour of the past 20 years, at fine scales (3 km grid) over the Dugway Proving Ground range.",
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"abstract": "Based on the consecutive high temporal resolution data observed at the special automatic weather stations ROSA on Beijing airport highway during 2006-2007, the modeling of atmospheric visibility classification forecast model with the nonlinear Support Vector Machine method was discussed and evaluated in this paper. The evaluation result shows that the performance of the forecast model by the Support Vector Machine method was good. 40% of atmospheric visibility classification forecast is consistent with the observed data; more than 90% of the forecast classification errors is within one level (including equality). Moreover, in the future 3–48 h forecast the atmospheric visibility performed stably. The perfect forecast results verify that the Support Vector Machine method has strong capability of processing the nonlinear relationship between atmospheric visibility and meteorological factors.",
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"content": "Based on the consecutive high temporal resolution data observed at the special automatic weather stations ROSA on Beijing airport highway during 2006-2007, the modeling of atmospheric visibility classification forecast model with the nonlinear Support Vector Machine method was discussed and evaluated in this paper. The evaluation result shows that the performance of the forecast model by the Support Vector Machine method was good. 40% of atmospheric visibility classification forecast is consistent with the observed data; more than 90% of the forecast classification errors is within one level (including equality). Moreover, in the future 3–48 h forecast the atmospheric visibility performed stably. The perfect forecast results verify that the Support Vector Machine method has strong capability of processing the nonlinear relationship between atmospheric visibility and meteorological factors.",
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"title": "Hybrid Intelligent Systems, International Conference on",
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"abstract": "This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological prediction global models, observations at surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model and a neural network to obtain the wind speed forecast in an specific point of the wind park. In the experiments carried out, we present some results of wind speed forecast in two aero-generators in a wind park at the south east of Spain. The results are encouraging, and show that our hybrid weather forecast models-neural network approach is able to obtain good short-term predictions of wind speed at specific points.",
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"content": "This paper presents the hybridization of global and mesoscale weather forecasting models with neural networks in order to tackle a problem of short-term wind speed prediction. The mean hourly wind speed forecast at aero-generators in a wind park is an important parameter used to predict the total energy production of the park. Our model for short-term wind speed forecast integrates two different meteorological prediction global models, observations at surface level and in different heights using atmospheric soundings. Also, it includes a mesoscale prediction model and a neural network to obtain the wind speed forecast in an specific point of the wind park. In the experiments carried out, we present some results of wind speed forecast in two aero-generators in a wind park at the south east of Spain. The results are encouraging, and show that our hybrid weather forecast models-neural network approach is able to obtain good short-term predictions of wind speed at specific points.",
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"abstract": "Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield forecast rely on NDVI (Normalized Difference Vegetation Index) data which, besides being time-consuming to acquire and process, only allows forecasting once crop season has already started. To bring scalability for yield forecast, in the present paper we describe a system that incorporates satellite-derived precipitation and soil properties datasets, seasonal climate forecasting data from physical models and other sources to produce a pre-season prediction of soybean/maize yield-with no need of NDVI data. This system provides significantly useful results by the exempting the need for high-resolution remote-sensing data and allowing farmers to prepare for adverse climate influence on the crop cycle. In our studies, we forecast the soybean and maize yields for Brazil and USA, which corresponded to 44% of the world's grain production in 2016. Results show the error metrics for soybean and maize yield forecasts are comparable to similar systems that only provide yield forecast information in the first weeks to months of the crop cycle.",
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"content": "Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield forecast rely on NDVI (Normalized Difference Vegetation Index) data which, besides being time-consuming to acquire and process, only allows forecasting once crop season has already started. To bring scalability for yield forecast, in the present paper we describe a system that incorporates satellite-derived precipitation and soil properties datasets, seasonal climate forecasting data from physical models and other sources to produce a pre-season prediction of soybean/maize yield-with no need of NDVI data. This system provides significantly useful results by the exempting the need for high-resolution remote-sensing data and allowing farmers to prepare for adverse climate influence on the crop cycle. In our studies, we forecast the soybean and maize yields for Brazil and USA, which corresponded to 44% of the world's grain production in 2016. Results show the error metrics for soybean and maize yield forecasts are comparable to similar systems that only provide yield forecast information in the first weeks to months of the crop cycle.",
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"proceeding": {
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"title": "2014 IEEE International Conference on High Performance Computing and Communications (HPCC), 2014 IEEE 6th International Symposium on Cyberspace Safety and Security (CSS) and 2014 IEEE 11th International Conference on Embedded Software and Systems (ICESS)",
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"abstract": "Over the past decade, the Web has been transformed from a collection of static HTML pages to a complex, distributed computing platform, as evidenced of the success are sites such as Facebook and YouTube. This transformation has been enabled primarily by the continuous development of Web applications. Inevitably-due to the Internet architecture-Web applications must rely on the servers so as to process and store confidential or user sensitive information. However, anyone who gains access to the server whether is a curious administrator, an attacker or even the government may obtain the data stored on the server or the server's database. This paper investigates ways to improve client-side security, through encryption, to keep user data private in the face of threats of various Web server compromises. A Web Application for online-note taking named Note Locker has been developed for this purpose by combining not only multiple security mechanisms but also modern Web development techniques to achieve confidentiality, integrity and a high level of user experience. The application is developed using open source products and eliminates possible attacks such as eavesdropping, sniffing, wiretapping, man-in-the-middle, and session hijacking of sensitive data by encrypting the data on the client side before sent for storage on the server.",
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"content": "Over the past decade, the Web has been transformed from a collection of static HTML pages to a complex, distributed computing platform, as evidenced of the success are sites such as Facebook and YouTube. This transformation has been enabled primarily by the continuous development of Web applications. Inevitably-due to the Internet architecture-Web applications must rely on the servers so as to process and store confidential or user sensitive information. However, anyone who gains access to the server whether is a curious administrator, an attacker or even the government may obtain the data stored on the server or the server's database. This paper investigates ways to improve client-side security, through encryption, to keep user data private in the face of threats of various Web server compromises. A Web Application for online-note taking named Note Locker has been developed for this purpose by combining not only multiple security mechanisms but also modern Web development techniques to achieve confidentiality, integrity and a high level of user experience. The application is developed using open source products and eliminates possible attacks such as eavesdropping, sniffing, wiretapping, man-in-the-middle, and session hijacking of sensitive data by encrypting the data on the client side before sent for storage on the server.",
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"title": "BabelRef: Detection and renaming tool for cross-language program entities in dynamic web applications",
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"abstract": "In a dynamic web application, client-side code is often dynamically generated from server-side code. Client-side program entities such as HTML presentation elements and Javascript functions/variables are embedded within server-side string literals or variables' values. However, existing tools for code maintenance such as automatic renaming support only work for program entities in a single language on either the server side or the client side. In this paper, we introduce BabelRef, a novel tool that is able to automatically identify and rename client-side program entities and their references that are embedded within server-side code.",
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"abstract": "Clinical data describing a patient's health status can be multi-labelled. For example, a clinical record describing patient suffering from cough and fever should be tagged with both two disease labels. These co-occurred labels often have interrelation which can be exploited to improve disease classifications. In this work, we treat the categorization of free clinical text as a multi-label learning problem. However, we discover that some commonly used multi-label learning methods might suffer from some severe side effects in exploiting complicated disease label relation, such as over-exploitation of label relation and error-propagation in label prediction. Based on these findings, we propose a novel multi-label learning algorithm called Ensemble of Sampled Classifier Chains (ESCC) to improve clinical text data classification. ESCC automatically learns to select relevant disease information that is helpful to improve classification performance when exploiting possible disease relation. In our conducted experiments, ESCC shows strong advantages over other state-of-the-art multi-label algorithms on medical text data with significant improvement in performance. The proposed algorithm is promising in mining knowledge from a wide range of multi-label medical text data.",
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"content": "Clinical data describing a patient's health status can be multi-labelled. For example, a clinical record describing patient suffering from cough and fever should be tagged with both two disease labels. These co-occurred labels often have interrelation which can be exploited to improve disease classifications. In this work, we treat the categorization of free clinical text as a multi-label learning problem. However, we discover that some commonly used multi-label learning methods might suffer from some severe side effects in exploiting complicated disease label relation, such as over-exploitation of label relation and error-propagation in label prediction. Based on these findings, we propose a novel multi-label learning algorithm called Ensemble of Sampled Classifier Chains (ESCC) to improve clinical text data classification. ESCC automatically learns to select relevant disease information that is helpful to improve classification performance when exploiting possible disease relation. In our conducted experiments, ESCC shows strong advantages over other state-of-the-art multi-label algorithms on medical text data with significant improvement in performance. The proposed algorithm is promising in mining knowledge from a wide range of multi-label medical text data.",
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"abstract": "The large volume of information stored in electronical health records is very valuable in the medical field, e.g., for clinical research and administrative purposes. However, health care professionals still face difficulties to recover and select relevant data. Although literature has investigated the influence of lexical, syntactical and semantic parameters in information retrieval techniques, few studies explore intentions as explicit users' actions in information recovery. This article studies means of considering intentions in search engines. We define a method with an original algorithm to properly rank search results from a set of electronical health records. This investigation relies on well-established theories to categorize several types of intentions. The proposed technique is based on knowledge representation models for annotating meanings and intentions in medical records. Achieved results are illustrated in scenarios based on real medical cases.",
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"abstract": "To determine the most effective book recommendation method for libraries, we conducted a recommendation experiment using (1) collaborative filtering based on the library loan records, (2) association rule mining based on the same data, and (3) Amazon. The library loan records of a certain university library for the period 2006 to 2011 were used. We recommended books to 33 students and asked them to describe the books' level of interest. The results show that books recommended by collaborative filtering were least favorably evaluated, followed by those recommended by association rule and Amazon. Collaborative filtering carries the risk of breaching users' privacy and its computational costs are higher compared to those of association rule mining. Therefore, if we recommend books based on library loan records, association rule mining should be adopted instead of collaborative filtering. In addition, given the fact that the recommendations by Amazon were most favorably evaluated, utilization of Amazon should also be considered.",
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"content": "To determine the most effective book recommendation method for libraries, we conducted a recommendation experiment using (1) collaborative filtering based on the library loan records, (2) association rule mining based on the same data, and (3) Amazon. The library loan records of a certain university library for the period 2006 to 2011 were used. We recommended books to 33 students and asked them to describe the books' level of interest. The results show that books recommended by collaborative filtering were least favorably evaluated, followed by those recommended by association rule and Amazon. Collaborative filtering carries the risk of breaching users' privacy and its computational costs are higher compared to those of association rule mining. Therefore, if we recommend books based on library loan records, association rule mining should be adopted instead of collaborative filtering. In addition, given the fact that the recommendations by Amazon were most favorably evaluated, utilization of Amazon should also be considered.",
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"abstract": "Exploring publications in academia usually entails browsing a collection of published literature through either bibliographic databases or full featured digital libraries. Bibliographic databases contain an organized collection of references to published literature, whereas digital libraries host publications in full-text. Although lacking full-text content of a publication, bibliographies contain rich metadata such as publication titles, venues or contributing authors that offer clues to the search process. DBLP, one of the computer science bibliographic databases, supports publication exploration by providing interfaces for finding specific bibliographic records or collections of records by a specific author. However, it lacks means for deep search process. In this paper, we describe a tool called DBLPminer that indexes DBLP records by topic categories under the taxonomy of ACM Computing Classification System, and offers a prototype web application interface through which researchers can organize or search for DBLP records by topic categories, or find associated topics for a DBLP record. Preliminary analysis of the indexing algorithm's performance yields promising results. We also compare DBLPminer with several other bibliographic exploration tools.",
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"content": "Exploring publications in academia usually entails browsing a collection of published literature through either bibliographic databases or full featured digital libraries. Bibliographic databases contain an organized collection of references to published literature, whereas digital libraries host publications in full-text. Although lacking full-text content of a publication, bibliographies contain rich metadata such as publication titles, venues or contributing authors that offer clues to the search process. DBLP, one of the computer science bibliographic databases, supports publication exploration by providing interfaces for finding specific bibliographic records or collections of records by a specific author. However, it lacks means for deep search process. In this paper, we describe a tool called DBLPminer that indexes DBLP records by topic categories under the taxonomy of ACM Computing Classification System, and offers a prototype web application interface through which researchers can organize or search for DBLP records by topic categories, or find associated topics for a DBLP record. Preliminary analysis of the indexing algorithm's performance yields promising results. We also compare DBLPminer with several other bibliographic exploration tools.",
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"title": "Automatic Extraction of Dublin Core Metadata from Presidential E-records",
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"abstract": "This paper describes how methods of natural language processing, grammatical description and parsing can be uses to recognize the document types of records distributed by the White House Press Office. It is also described how Dublin Core metadata can be extracted from these records to improve access to those records via Faceted Search. The applications being developed have broad potential use in the Presidential Libraries. Research issues being explored include automatic induction of grammars for defining document types and automatic methods for identifying related presidential records.",
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"authors": [
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"affiliation": "University of Maryland,College of Information Studies,College Park,USA",
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"title": "A CiteSeerX-Based Dataset for Record Linkage and Metadata Extraction",
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"abstract": "Bitcoin has created a new exchange paradigm within which financial transactions can be trusted without an intermediary. This premise of a free decentralized transactional network however requires, in its current implementation, unrestricted access to the ledger for peer-based transaction verification. A number of studies have shown that, in this pseudonymous context, identities can be leaked based on transaction features or off-network information. In this work, we analyze the information revealed by the pattern of transactions in the neighborhood of a given entity transaction. By definition, these features which pertain to an extended network are not directly controllable by the entity, but might enable leakage of information about transacting entities. We define a number of new features relevant to entity characterization on the Bitcoin Blockchain and study their efficacy in practice. We show that even a weak attacker with shallow data mining knowledge is able to leverage these features to characterize the entity properties.",
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"title": "Probing the Mystery of Cryptocurrency Exchange: The Case Study Based on Mt.Gox",
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"abstract": "The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.",
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"content": "The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.",
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"normalizedAbstract": "The birth of Bitcoin has created the cryptocurrency exchange, the average daily trading volume of cryptocurrency exchanges is now more than 100 billion. Cryptocurrency exchanges serve as a place for users to exchange cryptocurrencies, acting as a bridge between the blockchain ecosystem and the real world. Based on the transaction mechanism, cryptocurrency exchanges can be divided into centralized exchanges(CEXs) and decentralized exchanges(DEXs). CEXs still hold the dominant position, and we focus on Mt.Gox with the leaked dataset. By preprocessing the data, a usable internal dataset was obtained. To better study CEX, we further provide a comprehensive analysis of Mt.Gox based on three types of records and conclude its characteristics. Finally, we propose a matching method for on-chain and off-chain data, which restores the complete transaction path of the transaction account and some strange transaction phenomena are discovered. The results of this experiment showed that our algorithm can find addresses on blockchain and de-anonymize to a certain extent.",
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"affiliation": "Sun Yat-sen University,School of Computer Science and Engineering,Guangzhou,China",
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"title": "Multi-Class Bitcoin-Enabled Service Identification Based on Transaction History Summarization",
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"abstract": "In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.",
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{
"abstractType": "Regular",
"content": "In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.",
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"normalizedAbstract": "In recent years, Bitcoin has been used for many services and purposes, e.g. gambling, marketplace, but also even as an investment scam. In order to clarify how Bitcoin is used, it is in great importance to identify what kind of services are operated by Bitcoin addresses. In this paper, we propose a multiclass service identification scheme in Bitcoin based on novel transaction history summarization. Our novelty is to propose how transaction history is retrieved and how the retrieved transactions are processed for better identification. When a Bitcoin address is given, the characteristics of its transaction history is calculated as features. Then, the set of calculated features is fed into a supervised classifier and the services operated by the given Bitcoin addresses are identified among seven major services: (i) exchange, (ii) faucet, (iii) gambling, (iv) investment scam, (v) marketplace, (vi) mining pool, and (vii) mixer. To our knowledge, we are the first to propose a multi-class identification. We show that our scheme achieves 72 % of accuracy through performance evaluation with more than 26,000 Bitcoin addresses that have been used for seven services/purposes from Jan. 2009 to Feb. 2017.",
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"affiliation": "Dept. of Information and Computer Science, Keio University, Japan",
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"affiliation": "National and Kapodistrian University of Athens, Greece",
"fullName": "P. Takis Mathiopoulos",
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"abstract": "As an emerging payment method, bitcoin is receiving growing popularity for the different characteristics it shares with conventional fiat currencies. But the pseudonymous nature of bitcoin brings difficulties for regulators to effectively monitor bitcoin-related financial crimes. In this paper, we present an interactive system to visualize the relationship between bitcoin accounts, namely BitVis. With BitVis, users can easily filter transactions on demand, interact with the transaction networks to look for useful information, and analyze behavior of bitcoin accounts. Via BitVis, financial regulators can conveniently track suspicious accounts, while personal investors can easily investigate the activities of an interested account.",
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"authors": [
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"doi": "10.1109/ICOIN48656.2020.9016617",
"title": "Mapping Out Bitcoin’s Pseudonymous actors",
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"abstract": "The Bitcoin system is a pseudo-anonymous payment system and cryptocurrency, which can separate the virtual Bitcoin users' identities from any real-world identities. In this work, we developed a methodology on how to extract information about the real-world users behind Bitcoin transactions from the circulating data in the network. We focus on determining information about Bitcoin users ' physical location by examining users' spending habits. The extracted features are trained by a customized un-supervised clustering algorithm that combines K-Means and DBSCAN algorithms. The results show that over 70% of high valued transaction belong to gambling services, while almost 15% of them belongs to mining pools and exchanges services. Also, we notice that 7% of the discovered antonymous systems (AS) contained only one single Bitcoin node.",
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"affiliation": "Keimyung University,dept. of Computer Engineering,Daegu,Korean",
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"title": "An Analysis of Transaction Handling in Bitcoin",
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"abstract": "Bitcoin has become the leading cryptocurrency system, but the limit on its transaction processing capacity has resulted in increased transaction fee and delayed transaction confirmation. As such, it is pertinent to understand and probably predict how transactions are handled by Bitcoin such that a user may adapt the transaction requests and a miner may adjust the block generation strategy and/or the mining pool to join. To this aim, the present paper introduces results from an analysis of transaction handling in Bitcoin.Specifically, the analysis consists of two parts. The first part is an exploratory data analysis revealing key characteristics in Bitcoin transaction handling. The second part is a predictability analysis intended to provide insights on transaction handling such as (i) transaction confirmation time, (ii) block attributes, and (iii) who has created the block. The result shows that some models do reasonably well for (ii), but surprisingly not for (i) or (iii).",
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"affiliation": "NTNU, Norwegian University of Science and Technology,Department of Information Security and Communication Technology,Trondheim,Norway",
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"affiliation": "NTNU, Norwegian University of Science and Technology,Department of Information Security and Communication Technology,Trondheim,Norway",
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"affiliation": "NTNU, Norwegian University of Science and Technology,Department of Information Security and Communication Technology,Trondheim,Norway",
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"abstract": "We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component Analysis (HR-PCA) algorithm that is tractable, robust to outliers and easily kernelizable. The resulted subspace has a bounded deviation from the desired one, and achieves optimality in the limit case where the portion of outliers goes to zero.",
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"content": "We consider the dimensionality-reduction problem for a contaminated data set in a very high dimensional space, i.e., the problem of finding a subspace approximation of observed data, where the number of observations is of the same magnitude as the number of variables of each observation, and the data set contains some outlying observations. We propose a High-dimension Robust Principal Component Analysis (HR-PCA) algorithm that is tractable, robust to outliers and easily kernelizable. The resulted subspace has a bounded deviation from the desired one, and achieves optimality in the limit case where the portion of outliers goes to zero.",
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"abstract": "Currently, network administrators must rely on labor-intensive processes for tracking network configurations and vulnerabilities, which requires a great deal of expertise and is error prone. The organization of networks and the inter dependencies of vulnerabilities are so complex as to make traditional vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we discover attack paths (organized as graphs) that convey the impact of individual and combined vulnerabilities on overall security. We provide sophisticated attack graph visualizations, with high-level overviews and detail drill down. Decision support capabilities let analysts make optimal tradeoffs between safety and availability, and show how to best apply limited security resources. We employ efficient algorithms that scale well to larger networks.",
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"content": "Currently, network administrators must rely on labor-intensive processes for tracking network configurations and vulnerabilities, which requires a great deal of expertise and is error prone. The organization of networks and the inter dependencies of vulnerabilities are so complex as to make traditional vulnerability analysis inadequate. We describe a Topological Vulnerability Analysis (TVA) approach that analyzes vulnerability dependencies and shows all possible attack paths into a network. From models of the network vulnerabilities and potential attacker exploits, we discover attack paths (organized as graphs) that convey the impact of individual and combined vulnerabilities on overall security. We provide sophisticated attack graph visualizations, with high-level overviews and detail drill down. Decision support capabilities let analysts make optimal tradeoffs between safety and availability, and show how to best apply limited security resources. We employ efficient algorithms that scale well to larger networks.",
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"abstract": "Open-source libraries save developers time and effort by providing them access to pre-written functions, objects, and methods. The adoption of such libraries follows the current trend of more widespread use of open-source software and components. However, like proprietary software, open-source software can also suffer from defects that can be exploited by attackers. Many of these vulnerabilities have been identified and documented and are stored in Common Vulnerabilities and Exposures (CVE) databases maintained by entities such as NIST. Developers of these open-source components have a responsibility to inform their users of the vulnerabilities that exist in their releases and of the patches that fix these vulnerabilities. Consistent documentation of CVEs is a prerequisite for mitigating these vulnerabilities, especially if an automated approach is taken. This study investigates how well-documented are the patches both in the CVE database, and within the Github commits of C language open-source libraries. The results show that a significant number of CVEs in the NIST database do not mention the existence of patches and that only a small subset of the libraries looked at document CVEs in their commits. This paper comes to the conclusion that mutually agreed upon standards when it comes to CVE documentation should be adopted by both developers of open-source software and the entities that update and maintain CVE databases.",
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"content": "Open-source libraries save developers time and effort by providing them access to pre-written functions, objects, and methods. The adoption of such libraries follows the current trend of more widespread use of open-source software and components. However, like proprietary software, open-source software can also suffer from defects that can be exploited by attackers. Many of these vulnerabilities have been identified and documented and are stored in Common Vulnerabilities and Exposures (CVE) databases maintained by entities such as NIST. Developers of these open-source components have a responsibility to inform their users of the vulnerabilities that exist in their releases and of the patches that fix these vulnerabilities. Consistent documentation of CVEs is a prerequisite for mitigating these vulnerabilities, especially if an automated approach is taken. This study investigates how well-documented are the patches both in the CVE database, and within the Github commits of C language open-source libraries. The results show that a significant number of CVEs in the NIST database do not mention the existence of patches and that only a small subset of the libraries looked at document CVEs in their commits. This paper comes to the conclusion that mutually agreed upon standards when it comes to CVE documentation should be adopted by both developers of open-source software and the entities that update and maintain CVE databases.",
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"normalizedAbstract": "Open-source libraries save developers time and effort by providing them access to pre-written functions, objects, and methods. The adoption of such libraries follows the current trend of more widespread use of open-source software and components. However, like proprietary software, open-source software can also suffer from defects that can be exploited by attackers. Many of these vulnerabilities have been identified and documented and are stored in Common Vulnerabilities and Exposures (CVE) databases maintained by entities such as NIST. Developers of these open-source components have a responsibility to inform their users of the vulnerabilities that exist in their releases and of the patches that fix these vulnerabilities. Consistent documentation of CVEs is a prerequisite for mitigating these vulnerabilities, especially if an automated approach is taken. This study investigates how well-documented are the patches both in the CVE database, and within the Github commits of C language open-source libraries. The results show that a significant number of CVEs in the NIST database do not mention the existence of patches and that only a small subset of the libraries looked at document CVEs in their commits. This paper comes to the conclusion that mutually agreed upon standards when it comes to CVE documentation should be adopted by both developers of open-source software and the entities that update and maintain CVE databases.",
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"abstract": "Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.",
"abstracts": [
{
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"content": "Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.",
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"normalizedAbstract": "Nowadays, vulnerabilities in open source software (OSS) are constantly emerging, posing a great threat to application security. Security patches are crucial in reducing the risk of OSS vulnerabilities. However, many of the vulnerabilities disclosed by CVE/NVD are not accompanied by security patches. Previous research has shown that the auxiliary information in CVE/NVD can aid in the matching of a vulnerability to appropriate commits. The state-of-art research proposed a rank-based approach based on the multiple dimensions of features extracted from the auxiliary information in CVE/NVD. However, this approach ignores the semantic features in the vulnerability descriptions and commit messages, making the model still have room for improvement. In this paper, we propose a novel ranking-based approach VCMATCH (Vulnerability-Commit Match). In addition to extracting the shallow statistical features between the vulnerability and the patch commit, VCMATCH extracts the deep semantic features of the vulnerability descriptions and commit messages. Besides, VCMATCH applies three classification models (i.e., XGBoost, LightGBM, CNN) and uses a voting-based rank fusion method to combine the results of the three models to generate a better result. We evaluate VCMATCH with 1,669 CVEs from 10 OSS projects. The experiment results show that VCMATCH can effectively identify security patches for OSS vulnerabilities in terms of Recall@K and Manual Effort@K, and outperforms the state-of-art model by a statistically significant margin.",
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"affiliation": "School of Computer and Computing Science, Zhejiang University City College,Hangzhou,China",
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"affiliation": "School of Computer and Computing Science, Zhejiang University City College,Hangzhou,China",
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"affiliation": "College of Computer Science and Technology, Zhejiang University,Hangzhou,China",
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"title": "Automated Extraction of Software Names from Vulnerability Reports using LSTM and Expert System",
"normalizedTitle": "Automated Extraction of Software Names from Vulnerability Reports using LSTM and Expert System",
"abstract": "Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.",
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"normalizedAbstract": "Software vulnerabilities are closely monitored by the security community to timely address the security and privacy issues in software systems. Before a vulnerability is published by vulnerability management systems, it needs to be characterized to highlight its unique attributes, including affected software products and versions, to help security professionals prioritize their patches. Associating product names and versions with disclosed vulnerabilities may require a labor-intensive process that may delay their publication and fix, and thereby give attackers more time to exploit them. This work proposes a machine learning method to extract software product names and versions from unstructured CVE descriptions automatically. It uses Word2Vec and Char2Vec models to create context-aware features from CVE descriptions and uses these features to train a Named Entity Recognition (NER) model using bidirectional Long short-term memory (LSTM) networks. Based on the attributes of the product names and versions in previously published CVE descriptions, we created a set of Expert System (ES) rules to refine the predictions of the NER model and improve the performance of the developed method. Experiment results on real-life CVE examples indicate that using the trained NER model and the set of ES rules, software names and versions in unstructured CVE descriptions could be identified with F-Measure values above 0.95.",
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"Data Privacy",
"Expert Systems",
"Learning Artificial Intelligence",
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"Software Product Name Versions",
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"Vulnerability Management Systems",
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"Word 2 Vec",
"Analytical Models",
"Publishing",
"Software Systems",
"Software",
"Natural Language Processing",
"Delays",
"Security",
"Common Product Enumeration",
"Common Vulnerability And Exposures",
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"Software Product Name Extraction",
"Software Vulnerability"
],
"authors": [
{
"affiliation": "Sacred Heart University",
"fullName": "Igor Khokhlov",
"givenName": "Igor",
"surname": "Khokhlov",
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{
"affiliation": "Rochester Institute of Technology",
"fullName": "Ahmet Okutan",
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{
"affiliation": "Rochester Institute of Technology",
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{
"affiliation": "Rochester Institute of Technology",
"fullName": "Steven Simmons",
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{
"affiliation": "Rochester Institute of Technology",
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"abstract": "Security databases describe characteristics of discovered vulnerabilities in text for future studying and patching. However, due to different maintainers having different perspectives about vulnerabilities, they often describe the same vulnerability in different ways, creating obstacles for gathering comprehensive information about the vulnerabilities from different databases. To mitigate this problem, Common Vulnerability and Exposures (CVE) is established to identify each publicly disclosed vulnerability with a unique CVE id, and vulnerability databases by different vendors and organizations can reference the CVE ids in their vulnerability reports. In spite of the wide adoption of CVEs, traceability issues are still prevalent. Our empirical study on vulnerability traceability across four representative security databases (NVD, IBM X-Force, ExploitDB, Openwall) shows that there was a fast-increasing amount of CVE records, traceability delay, and missing issues become severe for the vulnerability databases. To address these issues, we develop an automatic traceability recovery method for recommending related external vulnerability reports to the reports in one database. As vulnerability reports from different databases differ in content details and length, our approach does not match the reports at the document level but extracts seven distinctive vulnerability key aspects that are widely present in vulnerability descriptions. As a proof of concept, we apply our methods to recommend the reports from IBM X-Force, ExploitDB and Openwall to the NVD report. We use NVD as the target because it is a de-facto standard vulnerability database that contains the most comprehensive list of vulnerabilities. Our experiments on a wide range of NLP methods show our aspect-level matching methods can achieve high MRR and accuracy for traceability recovery across heterogeneous vulnerability databases.",
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"abstract": "Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful \"0-day\" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those \"0-day\" attacks to at least \"n-day\" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.",
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{
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"content": "Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful \"0-day\" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those \"0-day\" attacks to at least \"n-day\" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.",
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"affiliation": "George Mason University",
"fullName": "Xinda Wang",
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"affiliation": "George Mason University",
"fullName": "Kun Sun",
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"affiliation": "Northrop Grumman",
"fullName": "Archer Batcheller",
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"affiliation": "George Mason University",
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"abstract": "Privacy is a term with multiple meanings. However, in the context of automated record-keeping systems which contain personal data about individuals, \"privacy\" refers to the rights of individuals regarding the collection, storage, processing, circulation, and use of personal information about themselves (1-4). Synonymous terms in use are \"fair information practices\" and, in many countries abroad, \"data protection\" (5,6). Privacy protection is achieved when privacy rights are granted to individuals by legislative means or voluntarily by record- keeping organizations, and their enforcement is implemented by procedural and technical means",
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"content": "Privacy is a term with multiple meanings. However, in the context of automated record-keeping systems which contain personal data about individuals, \"privacy\" refers to the rights of individuals regarding the collection, storage, processing, circulation, and use of personal information about themselves (1-4). Synonymous terms in use are \"fair information practices\" and, in many countries abroad, \"data protection\" (5,6). Privacy protection is achieved when privacy rights are granted to individuals by legislative means or voluntarily by record- keeping organizations, and their enforcement is implemented by procedural and technical means",
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"abstract": "Social network analysis (SNA) is increasingly attracting attentions from both academia and industrial areas. While revealing interesting properties and inferences from social network data is important, the protection of sensitive information of individuals is at the meanwhile a serious concern. In this paper, we study privacy preservation in interactive SNA settings, where the access to data is restricted to interactive queries, and the privacy of individuals is guaranteed by output perturbation. In particular, we dig into the problem of noisy answering of sub graph counting queries, while defending against the graph reconstruction attack that utilizes adaptive, incremental such queries to reconstruct the social graph. For the queries that we concern, applying the existing output perturbation mechanisms introduce too much noise to render the outputs useful. We solve this paradox by introducing ``seamless privacy'', a new notion of privacy that is shown to best fit the problem. Also, we propose a mechanism that achieves seamless privacy, and prove its correctness. Experiments on both real and synthetic data show that seamless privacy requires significantly less noise than its predecessors.",
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"content": "Social network analysis (SNA) is increasingly attracting attentions from both academia and industrial areas. While revealing interesting properties and inferences from social network data is important, the protection of sensitive information of individuals is at the meanwhile a serious concern. In this paper, we study privacy preservation in interactive SNA settings, where the access to data is restricted to interactive queries, and the privacy of individuals is guaranteed by output perturbation. In particular, we dig into the problem of noisy answering of sub graph counting queries, while defending against the graph reconstruction attack that utilizes adaptive, incremental such queries to reconstruct the social graph. For the queries that we concern, applying the existing output perturbation mechanisms introduce too much noise to render the outputs useful. We solve this paradox by introducing ``seamless privacy'', a new notion of privacy that is shown to best fit the problem. Also, we propose a mechanism that achieves seamless privacy, and prove its correctness. Experiments on both real and synthetic data show that seamless privacy requires significantly less noise than its predecessors.",
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"content": "Industrial Internet of Things (IIoT) is a relatively new area of research that utilises multidisciplinary and holistic approaches to develop smart solutions for complex problems in industrial environments. Designing applications for the IIoT is a non trivial issue and requires to address, among many others, technology concerns, the protection of personal data, and the privacy of individuals. In this review paper, we identify privacy-preserving solutions that have been proposed in the literature to safeguard the privacy of individuals being part, or interacting with, the IIoT environment. As such, it considers two main categories of the analysed protocols, i.e., the privacy-preserving data management and processing solutions, and the privacy-preserving authentication methods.",
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"title": "Finding Suspicious Activities in Financial Transactions and Distributed Ledgers",
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"abstract": "Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems.",
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"content": "Banks and financial institutions around the world must comply with several policies for the prevention of money laundering and in order to combat the financing of terrorism. Nowadays, there is a raise in the popularity of novel financial technologies such as digital currencies, social trading platforms and distributed ledger payments, but there is a lack of approaches to enforce the aforementioned regulations accordingly. Software tools are developed to detect suspicious transactions usually based on knowledge from experts in the domain, but as new criminal tactics emerge, detection mechanisms must be updated. Suspicious activity examples are scarce or nonexistent, hindering the use of supervised machine learning methods. In this paper, we describe a methodology for analyzing financial information without the use of ground truth. A user suspicion ranking is generated in order to facilitate human expert validation using an ensemble of anomaly detection algorithms. We apply our procedure over two case studies: one related to bank fund movements from a private company and the other concerning Ripple network transactions. We illustrate how both examples share interesting similarities and that the resulting user ranking leads to suspicious findings, showing that anomaly detection is a must in both traditional and modern payment systems.",
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"content": "HTTP-based automated software (auto-ware) are blooming in utilizing in reaching Internet users. Unfortunately, beside normal auto-ware such as for software updating purpose, auto-ware can be also abnormal processes acting as fraudulent advertising software, virus, spyware, and malicious bots. Malicious HTTP auto-ware will generate requests/access in communication with its server to mimic normal behavior and bypass firewall or IDS which almost allow HTTP-based data exchange. Because of that, in a private network perimeter, identifying which clients having suspicious HTTP action/auto-ware is really a big challenge. In this paper, by observing and analysis the HTTP communication behavior of malicious auto-ware, Access Variation Graph of domain/server is proposed to distinguish between normal and malicious domains/servers. Based on that, a network-based method proposal in mining suspicious domain is presented. From these results, network administrators are able to find out which clients having suspicious access/auto-ware.",
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"abstract": "One of the greatest challenges for managing organisational cyber security is the threat that comes from those who operate within the organisation. With entitled access and knowledge of organisational processes, insiders who choose to attack have the potential to cause serious impact, such as financial loss, reputational damage, and in severe cases, could even threaten the existence of the organisation. Security analysts therefore require sophisticated tools that allow them to explore and identify user activity that could be indicative of an imminent threat to the organisation. In this work, we discuss the challenges associated with identifying insider threat activity, along with the tools that can help to combat this problem. We present a visual analytics approach that incorporates multiple views, including a user selection tool that indicates anomalous behaviour, an interactive Principal Component Analysis (iPCA) tool that aids the analyst to assess the reasoning behind the anomaly detection results, and an activity plot that visualizes user and role activity over time. We demonstrate our approach using the Carnegie Mellon University CERT Insider Threat Dataset to show how the visual analytics workflow supports the Information-Seeking mantra.",
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"content": "One of the greatest challenges for managing organisational cyber security is the threat that comes from those who operate within the organisation. With entitled access and knowledge of organisational processes, insiders who choose to attack have the potential to cause serious impact, such as financial loss, reputational damage, and in severe cases, could even threaten the existence of the organisation. Security analysts therefore require sophisticated tools that allow them to explore and identify user activity that could be indicative of an imminent threat to the organisation. In this work, we discuss the challenges associated with identifying insider threat activity, along with the tools that can help to combat this problem. We present a visual analytics approach that incorporates multiple views, including a user selection tool that indicates anomalous behaviour, an interactive Principal Component Analysis (iPCA) tool that aids the analyst to assess the reasoning behind the anomaly detection results, and an activity plot that visualizes user and role activity over time. We demonstrate our approach using the Carnegie Mellon University CERT Insider Threat Dataset to show how the visual analytics workflow supports the Information-Seeking mantra.",
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"affiliation": "Department of Computer Science and Creative Technologies, University of the West of England, Bristol, UK",
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"abstract": "The surveillance of large sea areas often generates huge amounts of multidimensional data. Exploring, analyzing and finding anomalous behavior within this data is a complex task. Confident decisions upon the abnormality of a particular vessel behavior require a certain level of situation awareness that may be difficult to achieve when the operator is overloaded by the available information. Based on a visual analytics process model, we present a novel system that supports the acquisition of situation awareness and the involvement of the user in the anomaly detection process using two layers of interactive visualizations. The system uses an interactive data mining module that supports the insertion of the user's knowledge and experience in the creation, validation and continuous update of the normal model of the environment.",
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"abstract": "Global Domain Name System (DNS) traffic provides a unique perspective on domain names usage by both legitimate users and suspicious applications. Beyond conventional DNS analysis queries and responses altogether, in this paper we investigate domain name queries to identify suspicious network traffic at.br country code Top-Level Domain (ccTLD) authoritative name servers. By monitoring and modeling three DNS components into a direct graph, we expect that network operators are able to understand communication patterns between hosts and domain names, and the real purpose for a name resolution such as in mass Spam or in network reconnaissance attacks. This paper identifies relevant hosts for analysis among network traffic, reducing the number entities to be investigated.",
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"content": "Global Domain Name System (DNS) traffic provides a unique perspective on domain names usage by both legitimate users and suspicious applications. Beyond conventional DNS analysis queries and responses altogether, in this paper we investigate domain name queries to identify suspicious network traffic at.br country code Top-Level Domain (ccTLD) authoritative name servers. By monitoring and modeling three DNS components into a direct graph, we expect that network operators are able to understand communication patterns between hosts and domain names, and the real purpose for a name resolution such as in mass Spam or in network reconnaissance attacks. This paper identifies relevant hosts for analysis among network traffic, reducing the number entities to be investigated.",
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"title": "2017 IEEE 4th International Conference on Cyber Security and Cloud Computing (CSCloud)",
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"title": "Identifying Suspicious User Behavior with Neural Networks",
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"abstract": "The number of attacks that use sophisticated and complex methods increased lately. The main objective of these attacks is to largely infiltrate the target network and to stay undetected. Therefore, the attackers often use valid credentials and standard administrative tools to hide between legitimate user actions and to hinder detection. Most existing security systems, which use standard signature-based or anomaly-based approaches, are not able to identify this type of malicious activities. Furthermore, it is also most often not feasible to analyze user behavior manually, due to the complexity of this task and the high amount of different user actions. Thus, it is necessary to develop new automated approaches to identify suspicious user behavior. In this paper, we propose to use neural networks to analyze user behavior and to identify suspicious actions. Due to the fact that neural networks require suitable datasets to learn the difference between suspicious and benign actions, we describe a behavioral simulation system to generate reasonable datasets. These datasets use different behavioral features to describe log-on and log-off activities of users. To identify suitable neural network models for user behavior analysis, we evaluate and compare 16,275 different feed-forward neural networks with three different datasets and 75 recurrent neural networks with one dataset. The results show that the used dataset and the complexity of a model are crucial to achieve a high accuracy. Appropriate models, which also consider context behavior information, are able to automatically classify before unseen user actions with an accuracy of up to 98 %.",
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"content": "The number of attacks that use sophisticated and complex methods increased lately. The main objective of these attacks is to largely infiltrate the target network and to stay undetected. Therefore, the attackers often use valid credentials and standard administrative tools to hide between legitimate user actions and to hinder detection. Most existing security systems, which use standard signature-based or anomaly-based approaches, are not able to identify this type of malicious activities. Furthermore, it is also most often not feasible to analyze user behavior manually, due to the complexity of this task and the high amount of different user actions. Thus, it is necessary to develop new automated approaches to identify suspicious user behavior. In this paper, we propose to use neural networks to analyze user behavior and to identify suspicious actions. Due to the fact that neural networks require suitable datasets to learn the difference between suspicious and benign actions, we describe a behavioral simulation system to generate reasonable datasets. These datasets use different behavioral features to describe log-on and log-off activities of users. To identify suitable neural network models for user behavior analysis, we evaluate and compare 16,275 different feed-forward neural networks with three different datasets and 75 recurrent neural networks with one dataset. The results show that the used dataset and the complexity of a model are crucial to achieve a high accuracy. Appropriate models, which also consider context behavior information, are able to automatically classify before unseen user actions with an accuracy of up to 98 %.",
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