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Generative pre-trained transformers (GPT's) are a type of large language machine learning model that are unusually adept at producing novel, and coherent, natural language. In this study the ability of GPT models to generate novel and correct versions, and notably very insecure versions, of implementations of the ...
Tracking is a highly privacy-invasive data collection practice that has been ubiquitous in mobile apps for many years due to its role in supporting advertising-based revenue models. In response, Apple introduced two significant changes with iOS 14: App Tracking Transparency (ATT), a mandatory opt-in system for enablin...
This paper provides a proof of concept for using SRAM based Physically Unclonable Functions (PUFs) to generate private keys for IoT devices. PUFs are utilized, as there is inadequate protection for secret keys stored in the memory of the IoT devices.
Automatically analyzing information flow within Android applications that rely on cryptographic operations with their computational security guarantees imposes formidable challenges that existing approaches for understanding an app's behavior struggle to meet. These approaches do not distinguish cryptographic and ...
The rapid evolution of blockchain technology has brought together stakeholders from fundamentally different backgrounds. The result is a diverse ecosystem, as exemplified by the development of a wide range of different blockchain protocols.
GAN-generated image detection now becomes the first line of defense against the malicious uses of machine-synthesized image manipulations such as deepfakes. Although some existing detectors work well in detecting clean, known GAN samples, their success is largely attributable to overfitting unstable features such as f...
Knowledge files have been widely used in large language model (LLM) agents, such as GPTs, to improve response quality. However, concerns about the potential leakage of knowledge files have grown significantly.
Analyzing the security of cryptosystems under attacks based on the malicious modification of memory registers is a research topic of high importance. This type of attacks may affect the randomness of the secret parameters by forcing a limited number of bits to a certain value which can be unknown to the attacker.
Malware authors often employ code obfuscations to make their malware harder to detect. Existing tools for generating obfuscated code often require access to the original source code (e.g., C++ or Java), and adding new obfuscations is a non-trivial, labor-intensive process.
Secure Multi-Party Computation (MPC) offers a practical foundation for privacy-preserving machine learning at the edge, with MPC commonly employed to support nonlinear operations. These MPC protocols fundamentally rely on Oblivious Transfer (OT), particularly Correlated OT (COT), to generate correlated randomness esse...
Vehicular ad-hoc networks (VANETs) are one of the most important components in an Intelligent Transportation System (ITS), which aims to provide information communication between vehicles. A safety-critical vehicular communication requires security, privacy, auditability, and efficiency.
Cyber-Physical Systems (CPS) integrate physical and embedded systems with information and communication technology systems, monitoring and controlling physical processes with minimal human intervention. The connection to information and communication technology exposes CPS to cyber risks.
The Global Financial Crisis of 2008, caused by the accumulation of excessive financial risk, inspired Satoshi Nakamoto to create Bitcoin. Now, more than ten years later, Decentralized Finance (DeFi), a peer-to-peer financial paradigm which leverages blockchain-based smart contracts to ensure its integrity and security...
In this paper, a novel data hiding technique is proposed, as an improvement over the Fibonacci LSB data-hiding technique proposed by Battisti et al,based on decomposition of a number (pixel-value) in sum of natural numbers. This particular representation again generates a different set of (virtual) bit-planes altogeth...
A common form of phishing training in organizations is the use of simulated phishing emails to test employees' susceptibility to phishing attacks, and the immediate delivery of training material to those who fail the test. This widespread practice is dubbed embedded training; however, its effectiveness in decreasi...
Searchable Symmetric Encryption (SSE) allows a data owner to securely outsource its encrypted data to a cloud server while maintaining the ability to search over it and retrieve matched documents. Most existing SSE schemes leak which documents are accessed per query, i.e., the so-called access pattern, and thus are vu...
We present a principled technique for reducing the lattice and matrix size in some applications of Coppersmith's lattice method for finding roots of modular polynomial equations. Motivated by ideas from machine learning, it relies on extrapolating patterns from the actual behavior of Coppersmith's attack for s...
Computer malware and biological pathogens often use similar mechanisms of infections. For this reason, it has been suggested to model malware spread using epidemiological models developed to characterize the spread of biological pathogens.
The surge in Internet of Things (IoT) devices and data generation highlights the limitations of traditional cloud computing in meeting demands for immediacy, Quality of Service, and location-aware services. Fog computing emerges as a solution, bringing computation, storage, and networking closer to data sources.
Machine Learning (ML) alleviates the challenges of high-dimensional data analysis and improves decision making in critical applications like healthcare. Effective cancer type from high-dimensional genetic mutation data can be useful for cancer diagnosis and treatment, if the distinguishable patterns between cancer typ...
To encrypt sensitive information existing in a color DICOM images, a medical privacy protection scheme (called as MPPS) based on chaos and DNA coding was proposed by using two coupled chaotic systems to produce cryptographic primitives. Relying on some empirical analyses and experimental results, the designers of MPPS...
Deep neural networks (DNNs) are vulnerable to the \emph{backdoor attack}, which intends to embed hidden backdoors in DNNs by poisoning training data. The attacked model behaves normally on benign samples, whereas its prediction will be changed to a particular target label if hidden backdoors are activated.
Ethereum smart contracts hold tens of billions of USD in DeFi and NFTs, yet comprehensive security analysis remains difficult due to unverified code, proxy-based architectures, and the reliance on manual inspection of complex execution traces. Existing approaches fall into two main categories: anomaly transaction dete...
Smart Contracts (SCs) in Ethereum can automate tasks and provide different functionalities to a user. Such automation is enabled by the `Turing-complete' nature of the programming language (Solidity) in which SCs are written.
The emergence of quantum computing and Shor's algorithm necessitates an imminent shift from current public key cryptography techniques to post-quantum robust techniques. NIST has responded by standardising Post-Quantum Cryptography (PQC) algorithms, with ML-KEM (FIPS-203) slated to replace ECDH (Elliptic Curve Dif...
We study a variant of the source identification game with training data in which part of the training data is corrupted by an attacker. In the addressed scenario, the defender aims at deciding whether a test sequence has been drawn according to a discrete memoryless source $X \sim P_X$, whose statistics are known to h...
Network telescopes or "Darknets" provide a unique window into Internet-wide malicious activities associated with malware propagation, denial of service attacks, scanning performed for network reconnaissance, and others. Analyses of the resulting data can provide actionable insights to security analysts that ca...
The dramatic increase of data breaches in modern computing platforms has emphasized that access control is not sufficient to protect sensitive user data. Recent advances in cryptography allow end-to-end processing of encrypted data without the need for decryption using Fully Homomorphic Encryption (FHE).
Blockchain technology supports the generation and record of transactions, and maintains the fairness and openness of the cryptocurrency system. However, many fraudsters utilize smart contracts to create fraudulent Ponzi schemes for profiting on Ethereum, which seriously affects financial security.
Large Language Models (LLMs) have the potential to significantly enhance threat intelligence by automating the collection, preprocessing, and analysis of threat data. However, the usability of these tools is critical to ensure their effective adoption by security professionals.
As the COVID-19 pandemic started triggering widespread lockdowns across the globe, cybercriminals did not hesitate to take advantage of users' increased usage of the Internet and their reliance on it. In this paper, we carry out a comprehensive measurement study of online social engineering attacks in the early mo...
Dynamic wireless power transfer provides means for charging Electric Vehicles (EVs) while driving, avoiding stopping for charging and hence fostering their widespread adoption. Researchers devoted much effort over the last decade to provide a reliable infrastructure for potential users to improve comfort and time mana...
The goal of this paper is to analyze the behavior and intent of recent types of privacy invasive Android adware. There are two recent trends in this area: more financial motives instead of ego motives, and the development of more dynamic analysis tools.
Federated learning (FL) is vulnerable to poisoning attacks, where malicious clients manipulate their updates to affect the global model. Although various methods exist for detecting those clients in FL, identifying malicious clients requires sufficient model updates, and hence by the time malicious clients are detecte...
Research seeks to apply Artificial Intelligence (AI) to scale and extend the capabilities of human operators to defend networks. A fundamental problem that hinders the generalization of successful AI approaches -- i.e., beating humans at playing games -- is that network defense cannot be defined as a single game with ...
Cloud computing is a popular distributed network and utility model based technology. Since in cloud the data is outsourced to third parties, the protection of confidentiality and privacy of user data becomes important.
In late 2017, a sudden proliferation of malicious JavaScript was reported on the Web: browser-based mining exploited the CPU time of website visitors to mine the cryptocurrency Monero. Several studies measured the deployment of such code and developed defenses.
With the growing amount of personal information exchanged over the Internet, privacy is becoming more and more a concern for users. One of the key principles in protecting privacy is data minimisation.
Simulated environments have proven invaluable in Autonomous Cyber Operations (ACO) where Reinforcement Learning (RL) agents can be trained without the computational overhead of emulation. These environments must accurately represent cybersecurity scenarios while producing the necessary signals to support RL training.
Data controllers manage immense data, and occasionally, it is released publically to help the researchers to conduct their studies. However, this publically shared data may hold personally identifiable information (PII) that can be collected to re-identify a person.
Existing defenses against adversarial attacks are typically tailored to a specific perturbation type. Using adversarial training to defend against multiple types of perturbation requires expensive adversarial examples from different perturbation types at each training step.
In today's digital age, our dependence on IoT (Internet of Things) and IIoT (Industrial IoT) systems has grown immensely, which facilitates sensitive activities such as banking transactions and personal, enterprise data, and legal document exchanges. Cyberattackers consistently exploit weak security measures and t...
Service meshes have emerged as an attractive DevOps solution for collecting, managing, and coordinating microservice deployments. However, current service meshes leave fundamental security mechanisms missing or incomplete.
Motivated by practical concerns in cryptography, we study pseudorandomness properties of permutations on $\{0,1\}^n$ computed by random circuits made from reversible $3$-bit gates (permutations on $\{0,1\}^3$). Our main result is that a random circuit of depth $\sqrt{n} \cdot \tilde{O}(k^3)$, with each layer consistin...
Natural or man-made disasters pose significant challenges for delivering critical relief to affected populations due to disruptions in critical infrastructures and logistics networks. Unmanned aerial vehicles (UAVs)-aided disaster relief networks (UDRNs) leverage UAVs to assist existing ground relief networks by swift...
Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996 (HIPAA) and recent state-level regulations, permits sharing de-i...
Identity Access Management (IAM) is an area posing significant challenges, particularly in the context of remote connectivity and distributed or cloud-based systems. A wide range of technical solutions have been proposed by prior research, but the integration of these solutions in the commercial sector represent steps...
This paper proposed a DWT based Steganographic technique. Cover image is decomposed into four sub bands using DWT.
The system shows the error of "Bad character(s) in field Abstract" for no reason. Please refer to manuscript for the full abstract
Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation.
This paper presents a demo of our Security Toolbox to detect novel malware in Android apps. This Toolbox is developed through our recent research project funded by the DARPA Automated Program Analysis for Cybersecurity (APAC) project.
The Distributed Symmetric Key Establishment (DSKE) protocol provides secure secret exchange (e.g., for key exchange) between two honest parties that need not have had prior contact, and use intermediaries with whom they each securely share confidential data. We show the composable security of the DSKE protocol in the ...
Adversarial smart contracts, mostly on EVM-compatible chains like Ethereum and BSC, are deployed as EVM bytecode to exploit vulnerable smart contracts typically for financial gains. Detecting such malicious contracts at the time of deployment is an important proactive strategy preventing loss from victim contracts.
For secure communication it is not just sufficient to use strong cryptography with good and strong keys, but to actually have the assurance, that the keys in use for it are authentic and from the contact one is expecting to communicate with. Without that, it is possible to be subject to impersonation or man-in-the-mid...
This demo paper examines the susceptibility of Federated Learning (FL) systems to targeted data poisoning attacks, presenting a novel system for visualizing and mitigating such threats. We simulate targeted data poisoning attacks via label flipping and analyze the impact on model performance, employing a five-componen...
The rise of API-only access to state-of-the-art LLMs highlights the need for effective black-box jailbreak methods to identify model vulnerabilities in real-world settings. Without a principled objective for gradient-based optimization, most existing approaches rely on genetic algorithms, which are limited by their in...
This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain ...
Named Data Networking (NDN) secures network communications by requiring all data packets to be signed when produced. This requirement necessitates efficient and usable mechanisms to handle NDN certificate issuance and revocation, making these supporting mechanisms essential for NDN operations.
Despite their potential, recent research has demonstrated that LLM agents are vulnerable to prompt injection attacks, where malicious prompts are injected into the agent's input, causing it to perform an attacker-specified task rather than the intended task provided by the user. In this paper, we present PromptArm...
Security Hardening is the process of configuring IT systems to ensure the security of the systems' components and data they process or store. In many cases, so-called security-configuration guides are used as a basis for security hardening.
COVID-19 has hugely changed our lives, work, and interactions with people. With more and more online activities, people are easily exposed to privacy threats.
Side-channel analysis attacks, especially horizontal DPA and DEMA attacks, are significant threats for cryptographic designs. In this paper we investigate to which extend different multiplication formulae and randomization of the field multiplier increase the resistance of an ECC design against horizontal attacks.
Browser fingerprinting is a pervasive online tracking technique used increasingly often for profiling and targeted advertising. Prior research on the prevalence of fingerprinting heavily relied on automated web crawls, which inherently struggle to replicate the nuances of human-computer interactions.
Blockchain-based cryptocurrencies, facilitating the convenience of payment by providing a decentralized online solution, have not been widely adopted so far due to slow confirmation of transactions. Offline delegation offers an efficient way to exchange coins.
This research provides a critical analysis regarding the way blockchain is being implemented in the financial industry, highlighting its vital role in promoting green finance, guaranteeing compliance with regulations, improving supply chain finance, boosting decentralized finance (DeFi), and strengthening the Internet ...
We study the fundamental problem of guessing cryptographic keys, drawn from some non-uniform probability distribution $D$, as e.g. in LPN, LWE or for passwords. The optimal classical algorithm enumerates keys in decreasing order of likelihood.
In earlier work, we extend the Dolev-Yao model with assertions. We build on that work and add existential abstraction to the language, which allows us to translate common constructs used in voting protocols into proof properties.
We propose a secure and lightweight key based challenge obfuscation for strong PUFs. Our architecture is designed to be resilient against learning attacks.
By adding users as a new dimension to connectivity, on-body Internet-of-Things (IoT) devices have gained considerable momentum in recent years, while raising serious privacy and safety issues. Existing approaches to authenticate these devices limit themselves to dedicated sensors or specified user motions, undermining...
The recent surge in hardware security is significant due to offshoring the proprietary Intellectual property (IP). One distinct dimension of the disruptive threat is malicious logic insertion, also known as Hardware Trojan (HT).
Security analysts need to classify, search and correlate numerous images. Automatic classification tools improve the efficiency of such tasks.
Popular Ethereum wallets (like MetaMask) entrust centralized infrastructure providers (e.g., Infura) to run the consensus client logic on their behalf. As a result, these wallets are light-weight and high-performant, but come with security risks.
Machine Learning as a Service (MLaaS) allows clients with limited resources to outsource their expensive ML tasks to powerful servers. Despite the huge benefits, current MLaaS solutions still lack strong assurances on:
With the rapid development of large language models (LLMs), their applications have expanded into diverse fields, such as code assistance. However, the substantial size of LLMs makes their training highly resource- and time-intensive, rendering frequent retraining or updates impractical.
We propose SANSCrypt, a novel sequential logic encryption scheme to protect integrated circuits against reverse engineering. Previous sequential encryption methods focus on modifying the circuit state machine such that the correct functionality can be accessed by applying the correct key sequence only once.
Federated knowledge discovery and data mining are challenged to assess the trustworthiness of data originating from autonomous sources while protecting confidentiality and privacy. Truth-finding algorithms help corroborate data from disagreeing sources.
Integrated sensing and communication (ISAC) systems potentially encounter significant performance degradation in densely obstructed urban and non-line-of-sight scenarios, thus limiting their effectiveness in practical deployments. To deal with these challenges, this paper proposes a backscatter device (BD)-assisted IS...
New types of Trusted Execution Environment (TEE) architectures like TrustLite and Intel Software Guard Extensions (SGX) are emerging. They bring new features that can lead to innovative security and privacy solutions.
In the era of Industry 4.0, the growing need for secure and efficient communication systems has driven the development of fifth-generation (5G) networks characterized by extremely low latency, massive device connectivity and high data transfer speeds. However, the deployment of 5G networks presents significant securit...
Guttman presented a model-theoretic approach to establishing security goals in the context of Strand Space theory. In his approach, a run of the Cryptographic Protocol Shapes Analyzer (CPSA) produces models that determine if a goal is satisfied.
The number of Connected and Autonomous Vehicles (CAVs) is increasing rapidly in various smart transportation services and applications, considering many benefits to society, people, and the environment. Several research surveys for CAVs were conducted by primarily focusing on various security threats and vulnerabiliti...
Amidst the burgeoning landscape of database architectures, the surge in NoSQL databases has heralded a transformative era, liberating data storage from traditional relational constraints and ushering in unprecedented scalability. As organizations grapple with the escalating security threats posed by database breaches,...
Traditional steganographic techniques have often relied on manually crafted attributes related to image residuals. These methods demand a significant level of expertise and face challenges in integrating diverse image residual characteristics.
This work is based on the proposal of a deterministic randomness extractor of a random Diffie-Hellman element defined over two prime order multiplicative subgroups of a finite fields $\mathbb{F}_{p^n}$, $G_1$ and $G_2$. We show that the least significant bits of a random element in $G_1*G_2$, are indistinguishable fro...
The massive growth of the Internet of Things (IoT) as a network of interconnected entities [18], brings up new challenges in terms of privacy and security requirements to the traditional software engineering domain [4]. To protect the individuals' privacy, the FTC's Fair Information Practice Principles (FIPPs)...
Non-fungible tokens (NFTs) drive the prosperity of the Web3 ecosystem. By November 2023, the total market value of NFT projects reached approximately 16 billion USD.
The Internet of Vehicles (IoV) is formed by connecting vehicles to Internet of Things. It enables vehicles to ubiquitously access to the information of customers (drivers), suppliers, and even producers to structure an IoV-based industrial value chain.
Let $\mathcal{X}$ and $\mathcal{Y}$ be two sets and suppose that a set of participants $P=\{P_1,P_2,\dots,P_n\}$ would like to calculate the keyed hash value of some message $m\in\mathcal{X}$ known to a single participant in $P$ called the data owner. Also, suppose that each participant $P_i$ knows a secret value $x_i...
This paper proposes the use of sets of keys, together with corresponding identifiers, for use in wireless sensor networks and similar resource-constrained applications. A specific cryptographic scheme described in the paper is based on the use of a family of self-inverting matrices derived from the number theoretic Hi...
We describe a concept to employ Trusted Computing technology to secure Conditional Access Systems (CAS) for DVB. Central is the embedding of a trusted platform module (TPM) into the set-top-box or residential home gateway.
Internet of Things (IoT) is the interconnection of heterogeneous smart devices through the Internet with diverse application areas. The huge number of smart devices and the complexity of networks has made it impossible to secure the data and communication between devices.
While much research focuses on different methods to secure blockchain, information on the chain needs to be accessed by end-users to be useful. This position paper surveys different ways that end-users may access blockchains.
Recent advances in the blockchain research have been made in two important directions. One is refined resilience analysis utilizing game theory to study the consequences of selfish behaviors of users (miners), and the other is the extension from a linear (chain) structure to a non-linear (graphical) structure for perf...
High-speed interconnects, such as NVLink, are integral to modern multi-GPU systems, acting as a vital link between CPUs and GPUs. This study highlights the vulnerability of multi-GPU systems to covert and side channel attacks due to congestion on interconnects.
This chapter discusses the intricacies of cybersecurity agents' perception. It addresses the complexity of perception and illuminates how perception shapes and influences the decision-making process.
Generative AI (Gen AI) with large language models (LLMs) are being widely adopted across the industry, academia and government. Cybersecurity is one of the key sectors where LLMs can be and/or are already being used.
Game theory has long served as a foundational tool in cybersecurity to test, predict, and design strategic interactions between attackers and defenders. The recent advent of Large Language Models (LLMs) offers new tools and challenges for the security of computer systems; In this work, we investigate whether classical...
The Tor anonymity system provides online privacy for millions of users, but it is slower than typical web browsing. To improve Tor performance, we propose PredicTor, a path selection technique that uses a Random Forest classifier trained on recent measurements of Tor to predict the performance of a proposed path.
Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedded device software. With the rapid development of binary diffing techniques, they can achieve more and more accurate function matching and ...
Supervised Deep Learning requires plenty of labeled data to converge, and hence perform optimally for task-specific learning. Therefore, we propose a novel mechanism named DRo (for Deep Routing) for data-scarce domains like security.

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