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c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 7.1.3.7 Duty Cycle/Mechanical Scanning | A narrow beam scanning antenna only illuminates a given target area intermittently. The radar boresight scans a horizontal plane parallel to the ground. The antenna duty cycle depends on the antenna beam width in azimuth systems and is between 0,25 - 0,5 %. The scan rate is model dependant but ranges between 1 - 4 Hz. ... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 7.1.4 Receiver parameters | The infrastructure radar includes either monostatic (single antenna) for transmit and receive (H1) or is a bi-static, dual antenna configuration for the H2 & H3 examples. The radar receiver includes an active mixer that converts the Radio Frequency signal into an Intermediate Frequency range which covers 50 kHz to 5 MH... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 7.2 Information on relevant standard(s) | The following ETSI standards apply to short range radar equipment using the 76 - 77 GHz band: • ETSI EN 301 091-1 [i.5] "Short Range Devices; Transport and Traffic Telematics (TTT); Radar equipment operating in the 76 GHz to 77 GHz range; Harmonised Standard covering the essential requirements of article 3.2 of Directi... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 8 Radio spectrum request and justification | No change to the spectrum allocations, in terms of frequency bands, power limits, etc., is requested. The purpose of the present document is to seek clarity and a harmonised position on the applications and use cases of radar equipment in the 76 - 77 GHz band. In particular, the request is that applications for securit... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9 Regulations | |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9.1 Current regulations | Fixed infrastructure radars are in Annex 5 (TTT) of ERC/REC 70-03 [i.3] with a corresponding usage restriction in the EU Decision [i.4]. Frequency Band Power / Magnetic Field Spectrum access and mitigation requirements Modulation / occupied bandwidth ECC/ERC Deliverable Notes e1 76 - 77 GHz 55 dBm peak e.i.r.p. (see no... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9.2 Proposed regulation and justification | |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9.2.1 Additional applications | The applications described in clause 5.2 (Fixed Security and Safety Applications) raise an interesting question when FSSA is installed in locations such as airports and harbours. Is the application already harmonised as Transport and Traffic Telematics? If a radar illuminates a vehicle or a small boat, does the answer ... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9.2.2 Scanning antenna restriction | The reasons for mandating scanning antennas are discussed above in clause 5.2.5. It is noted that this provision arose purely for the purposes of mitigation towards vehicular radar. The proposal therefore is that the requirement for antennas of a scanning nature is applied only to roadside installations. One option is ... |
c61edf069c94e8cd635d6dbdeb17b902 | 104 052 | 9.2.3 Roadside meaning | Inevitably, the above proposals lead to the question of what counts as roadside and non-roadside. The following ideas are offered for consideration. Road A paved way accessible to the public on which motorised traffic routinely exceeds 100 vehicles per hour and which is not subject to a speed limit of 20 km/h or lower.... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 1 Scope | The present document analyses the mechanisms that use cryptography in the specifications under ETSI TC SET responsibility. It describes the potential changes for a responsible industry transition to Quantum-Safe technology. |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 2 References | |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 2.1 Normative references | Normative references are not applicable in the present document. |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. • In the case of a reference... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 3 Definition of terms, symbols and abbreviations | |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 3.1 Terms | Void. |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 3.2 Symbols | Void. |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 3.3 Abbreviations | For the purposes of the present document, the following abbreviations apply: AES Advanced Encryption Standard APDU Application Protocol Data Unit DAP Data Authentication Pattern ECC Elliptic Curve Cryptography KIc Key and algorithm Identifier for ciphering KID Key and algorithm IDentifier for RC/CC/DS PQC Post-Quantum ... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 4 General presentation | Cryptography has become part of our daily life, securing most of our electronic activities ranging from web browsing to mobile communications or payments. Although cryptography is a key component of digital security, it has never experienced the ever-faster cycle of attacks and patches that characterizes cybersecurity ... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5 Analysis of ETSI TC SET specifications | |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.1 ETSI TS 102 224 | ETSI TS 102 224 [i.1] describes the functional requirements of security mechanisms in conjunction with the Card Application Toolkit for the interface between a Network Entity and a UICC. Regarding the cryptographic mechanisms, ETSI TS 102 224 [i.1], clause 6.2.2,contains only two high level requirements which are still... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.2 ETSI TS 102 225 | ETSI TS 102 225 [i.2] specifies the structure of Secured Packets for different transport and security mechanisms. The following impacts are seen, together with remediation proposals for transitioning ETSI TS 102 225 [i.2] to post-quantum cryptography. ETSI ETSI TR 104 005 V1.1.1 (2025-07) 9 Table 1 Impacts Requirements... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.3 ETSI TS 102 226 | |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.3.1 Introduction | ETSI TS 102 226 [i.3] defines the remote management of the UICC based on the secured packet structures specified in ETSI TS 102 225 [i.2], i.e.: • SMS and CAT_TP based packet structures, also known as SCP80; • HTTP-based using TLS cipher suites, also known as SCP81 and defined by GlobalPlatform in Amendment B to the Gl... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.3.2 Analysis of the current content of ETSI TS 102 226 | The following impacts are seen, together with remediation proposals for transitioning ETSI TS 102 226 [i.3] to post-quantum cryptography. Table 2 Impacts Requirements to become Quantum-Safe Use of SCP81 According to GlobalPlatform PQC roadmap (see note), an update of Amendment B to the GlobalPlatform Card Specification... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.3.3 Other areas of improvement | |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 5.3.3.1 Secure Channel Protocol '04' (SCP04) | Secure Channel Protocol '04' (SCP04), defined by GlobalPlatform in Amendment K to the GlobalPlatform Card Specification [i.13] is designed to be crypto agile, i.e. algorithms may be replaced with less effort by other algorithms when vulnerabilities are found, or more secure algorithms become available. The current vers... |
b0a27e9110e6af2add8b26733a6c8444 | 104 005 | 6 Conclusion and way forward | The present document provides analysis regarding the mechanisms that use cryptography in the specifications under ETSI TC SET responsibility. Potential changes for a responsible transition to Quantum-Safe technology are described. However, the impact on performance which may be caused by the introduction of Quantum-Saf... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 1 Scope | The use of AI to facilitate the use cases may cause AI security and privacy issues specific to the telecom industry. The scope of this proposed work item will be to investigate security and privacy issues related to the use of AI in the telecom industry sector. Harmonisation with 3GPP work in SA1, SA2, and SA3 is antic... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 2 References | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 2.1 Normative references | Normative references are not applicable in the present document. |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 2.2 Informative references | References are either specific (identified by date of publication and/or edition number or version number) or non-specific. For specific references, only the cited version applies. For non-specific references, the latest version of the referenced document (including any amendments) applies. NOTE: While any hyperlinks i... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3 Definition of terms, symbols and abbreviations | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.1 Terms | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.1.1 Network Operations Lifecycle Phases | The present document adopts a commonly used nomenclature used for describing activities pertaining to the commissioning of a service, namely Day 0 - Day N. This approach and terms have been used in the ETSI Open Source MANO [i.5] project and their application explained in [i.6], [i.7], and [i.8] to describe the onboard... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.1.2 NIST AI Attack Taxonomy | AI supply chain: manipulation of training data or AI/ML model or AI/ML supporting software libraries evasion: manipulating data which results in misclassification or no detection poisoning : manipulating training data which results in model learning incorrectly privacy: extracting sensitive information model was traine... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.1.3 NIST AI Attacker Goals | abuse violation: abuse of a deployed AI/ML model to achieve attacker goals availability breakdown: degradation of AI/ML model performance during deployment integrity violation: erosion of model integrity to elicit incorrect results either through evasion or poisoning privacy compromise: discovery of information pertain... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.2 Symbols | Void. ETSI ETSI TR 104 051 V1.1.1 (2025-06) 8 |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 3.3 Abbreviations | ADRF Analytics Data Repository Function AF Application Function AI Artificial Intelligence AI/ML AF AI/ML Application Function AKMA Authentication and Key Management for Applications AMF Access Management Function AN Access Network AnLF Analytics Logical Function AS Access Stratum CI/CD Continuous Integration and Conti... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 4 Convention Description | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 4.1 Notation | For the purpose of the present document, the following notations apply: <information> stands for the "information" been exchanged or transmitted between different modules or via interfaces. [security component] stands for the "security components" described in clause 7 that has involved in the interactive procedures. (... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5 Overview | 5.1 Use of Generative AI vs. traditional AI in telecom providers' networks In many cases, traditional AI (as opposed to Generative AI (GenAI)) might be sufficient in telecom providers' networks. Many of the use cases described in further clauses (e.g. Anomaly detection, Customer churn prediction, Predictive maintenance... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2 ML functionality in telecom providers' networks | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.1 Data collection and preparation mechanisms | In the context of 5G / 3GPP-enabled standards, data collection forms part of the core functionality of the Management Data Analytics (MDA) capability of the network. More generally, the telecom provider should ensure the availability of structured, real-time operational and performance characteristics of their network,... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.2 Model engineering and evaluation mechanisms | Model engineering refers to the process of designing ML "pipelines" that convert raw data into actionable inferences by performing a set of transformations. This process is executed based on the insights of domain experts. In the context of a telecommunications network, this translates to the Network Operations Centre ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.3 Model deployment mechanisms | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.3.1 Introduction | Model deployment is often referred to as model distribution. To take advantage of the operators' Core Network (CN) computing resources, a model can be engineered and evaluated at the core network nodes or AI/ML Application Functions (AI/ML AF) outside of the operator's core network and deployed for execution to the Use... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.3.2 Model distribution using CP | The main advantage of model distribution using CP is its confidentiality, integrity, and replay protection over the air while protected with NAS security. Another advantage is full operator's control over CP and NAS security. However, a potentially large size of transferred models can adversely affect CP availability f... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.3.3 Model distribution using UP | UP use for model distribution has the advantage of being able to accommodate large ML model sizes. UP might be used with Application Layer security (e.g. end-to-end) and as such can be easily outsourced by an operator. However, being opaque to the operator is one of the main disadvantages. The user QoE is mainly attrib... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 5.2.3.4 Hybrid model distribution using CP and UP | Hybrid model distribution aims to remediate the CP-only and UP-only modes. Such a mode uses CP to set the cryptographic model distribution control that is shared between the home operator, serving operator, and model owner/custodian. After the CP-assisted framework for model transfer is set, the UP is utilized to distr... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.1 System monitoring | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.1.1 Introduction | System monitoring could be seen as a prerequisite to gathering information on the status of the system to perform further investigations as described in the subsequent clauses. The data and information that is monitored may be on different layers in the system, i.e. in the general availability of resources. For example... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.1.2 Anomaly detection | System monitoring is a prerequisite for Anomaly Detection and for developing the definition of the normal state and behaviour of the system to draw a comparison to events or behaviour that deviate from the normal state. There is some basic Anomaly Detection already defined within the scope of the network analytics and ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.1.3 Root cause identification | The analytics of the MDA can already contain specific root cause identification of the detected issues. There are already several requirements on the MDA capabilities to provide a root cause identification e.g. network slice throughput issue(s), E2E latency issue, network slice load issues and recommended actions, ener... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.1.4 Predictive maintenance | Information from the MDA pertaining to the operational characteristics of the underlying network infrastructure may be used as inputs to NN models trained to predict expected hardware failure times and flag components for pre-emptive replacement or repair. For example, a model may be used to ingest Self-Monitoring, Ana... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2 Intelligent networks | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.1 Introduction | Traditionally, in the telecom context, Intelligent Network (IN) allows functionality to be distributed flexibly at a variety of nodes in and outside the network and allows the architecture to be modified to control the services. AI in telecom networking offers several key advantages that are transforming how networks a... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.2 Ability to self-heal | Given an identified (e.g. by Root Cause Identification subsystem) issue, remediation is typically performed through some combination of manual actions performed by a human, along with the application of infrastructure configuration changes, and occasionally, software code patches. The actions that need to be performed ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.3 Root cause identification | The analytics of the MDA can already contain specific root cause identification of the detected issues. There are already several requirements on the MDA capabilities to provide a root cause identification e.g. network slice throughput issue(s), E2E latency issue, network slice load issues and recommended actions, ener... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.4 Predictive maintenance | Information from the MDA pertaining to the operational characteristics of the underlying network infrastructure may be used as inputs to NN models trained to predict expected hardware failure times and flag components for pre-emptive replacement or repair. For example, a model may be used to ingest Self-Monitoring, Ana... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.5 Dynamic optimization | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.5.1 Mobility Optimization | Mobility Optimization is one of the use cases studied in [i.3] and specified in [i.4] and happens in the operational phase, i.e. Day 2. The use case aims to minimize performance loss due to unsuccessful or erroneous mobility management events. Mobility management is expected to guarantee the service-continuity during t... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.5.2 Load Balancing | The Load Balancing use case aims to distribute the load evenly among cells and areas of cells, to transfer part of the traffic from congested cells or congested areas of cells, or to offload users from one cell, cell area, carrier, or RAT to improve network performance. This can be done by means of optimization of hand... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.2.6 Automated network design | The following clause provides an example of an AI/ML pipeline dedicated to automated network design. It utilized the methodology and terminology that is defined in clause 3.1.1. ETSI ETSI TR 104 051 V1.1.1 (2025-06) 16 Network Requirements Analysis and Design tasks typically occur in the Day 0 phase. Requirements Gathe... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.3 Managed telecom services | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.3.1 Use cases | The following subclauses add detailed descriptions for managed telecom services use cases. These use cases mostly relate to Day 2 in the network lifecycle described in clause 3.1.1. |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.3.2 Ticket (e.g. trouble ticket, CR) classification and routing | In many cases, the tickets contain data entered by humans in natural language such as a description of the problem and the steps already taken. While frequently some initial classification of the problem is provided by the person opening the ticket, it can also be marked as 'other' or be imprecise. In any case, the LLM... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.3.3 Customer churn prediction | At a higher level, LLM-based sentiment analysis is also possible with the tickets (see clause 6.3.1) and other sources (e.g. the history of chats clients had with a company chatbot or social media posts). This can be combined with data obtained from network infrastructure measurements such as end-to-end delay or downti... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 6.3.4 Service (e.g. SLA) assurance | Service level assurance utilizes several elements mentioned previously, with monitoring and data collection being a starting point. AI/ML-based analysis of the data can predict, for example, a possible SLA violation (e.g. probability of exceeding an agreed end-to-end delay value within a given time frame is 90 %), anal... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.1 Attacks on System Monitoring | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.1.1 Introduction | It is envisioned that the compiled executable AI models will be used as AI Agents attached to network functions or form separate network functions themselves. AI agents that improperly or maliciously function may be potent attack vectors in telecom networks. Their instantiation and use need to be strictly monitored Eac... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.1.2 Anomaly detection | Most NN anomaly detection algorithms operate on time series data. Studies have demonstrated an attack on such models by inducing imperceptible perturbations into an input signal fed to the model [i.10]. This form of attack causes models to misclassify anomalous signals as normal operating behaviour with high confidence... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.1.3 Root cause identification | Targeting the MDA system can offer an attacker the opportunity to induce misdiagnosis of a problem root cause, thereby either triggering an erroneous response or distracting response teams to mask a secondary attack. The specific techniques that may be employed by an attacker depend on the RC identification mechanisms ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.1.4 Predictive maintenance | Similar to the case for stochastic MDA systems, NN models trained for predicting system failures may be compromised by poisoning or signal tampering. Time series models, which are typically employed for such monitoring are also particularly susceptible to perturbation attacks, whereby an attacker introduces specific, s... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.2 Attacks on Intelligent Networks | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.2.1 Introduction | During the runtime phase, an attack on the AI-powered network control plane can allow an adversary to disrupt the service in several ways. These include limiting the system's ability to correctly execute the reconciliation loop or tricking it into making incorrect decisions regarding resource management. Also, when AI ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.2.2 Ability to self-heal | Poisoning of the training data in the case of CoA identification can lead to a set of actions that ostensibly address a given issue in the system but leave unaddressed an exploitable weakness. In the case of CoA execution, the employed LLMs may be induced to generate code with vulnerabilities. This is possible even in ... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.2.3 Dynamic optimization | The time series nature of network performance optimization presents the risk of perturbation attacks similar to those possible on predictive maintenance systems. When applied to the mobility optimization use case, it would then be possible for an attacker to artificially degrade user experience in specific cells by tri... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.2.4 Automated Network Design | A potential for data extraction exists if an attacker is able to capture prompts given to the model, especially in the case that the model is provided as a public cloud SaaS. By repeating the prompts an attacker can recreate the output, or potentially expose confidential information fed to the model. This attack may be... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.3 Attacks on Managed Telecom Services | |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.3.0 Introduction | A successful attack on customer-related services can damage the operator's reputation and potentially escalate to both monetary and legal consequences. This can occur if there is a violation of contractual agreements or if a leak of personally identifiable information is discovered. ETSI ETSI TR 104 051 V1.1.1 (2025-06... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.3.1 Ticket (e.g. trouble ticket, CR) classification and routing | LLM models employed for ticket classification, especially those that are exposed to customer-originated tickets, are at high risk for prompt injection with the aims either of inducing privacy compromise or triggering hallucinatory behaviour. The latter represents a legal risk for the operator as, depending on the juris... |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.3.2 Customer churn prediction | Time series attack techniques such as smooth perturbation may be employed by an attacker to induce churn prediction models to present an overly optimistic estimate, thereby causing revenue loss. The mitigation of these attacks follows the strategy outlined for time series models. |
a65dbfd110e2620b04001bee98bdcd6e | 104 051 | 7.3.3 Service (e.g. SLA) assurance | Due to its compound nature consisting of sets of systems such as RCA, CoA generation, and CoA execution, the possible attacks, and the corresponding mitigation strategies, on SLA assurance systems may be considered as the union of the attacks and mitigations of their constituent systems. ETSI ETSI TR 104 051 V1.1.1 (20... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 1 Scope | The present document provides an assessment of the feasibility of using object-orientation in the development of standards, particularly when used in association with Message Sequence Charts (MSC), Specification and Description Language (SDL) defined in ITU-T Recommendations Z.120 [11], Z.100 [9] and Z.105 [10] and the... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 2 References | The following documents contain provisions which, through reference in this text, constitute provisions of the present document. • References are either specific (identified by date of publication, edition number, version number, etc.) or non-specific. • For a specific reference, subsequent revisions do not apply. • Fo... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 3 Definitions and abbreviations | |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 3.1 Definitions | For the purposes of the present document, the following definition applies: metamodel: a model that defines the language for expressing a model (from The UML Reference Manual [19]) |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 3.2 Abbreviations | For the purposes of the present document, the following abbreviations apply: ASN.1 Abstract Syntax Notation No 1 CASE Computer Assisted Software Engineering CORBA Common Object Request Broker Architecture CTMF Conformance Testing Methodology and Framework GDMO Guidelines for the Definition of Managed Objects IS Informa... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4 Feasibility study | The following activities were undertaken in order to evaluate the use of object-orientation in the ETSI standardization process and these are reported here: - review of the existing standardization process within ETSI; - review of UML methods and standardization; - application of UML to existing standards in three case... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.1 Standardization process | |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.2 ETSI Standardization Process | Before being able to assess the value of using object-orientation in the ETSI standardization process, it is necessary to review this process itself. The ETSI standardization process has evolved over a number of years and although methods exist and are well documented for the development of many different types of stan... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3 Introduction to Object-Orientation (OO) | |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1 The Unified Modelling Language (UML) | The Unified Modelling Language (UML) is a language for visualizing, specifying, constructing and documenting software systems and has been developed using best practices from existing object-oriented methods. It is standardized by Industry in the Object Management Group (OMG). The UML uses diagrams to visualize the str... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.1 The Architecture of a System | The architecture of a software-intensive system can have five interlocking views: - the Use Case view: specifies the forces that shape the system's architecture and describes the behaviour of the system as seen by its users, analysts and testers; - the Design view: collects the vocabulary (as classes, interfaces and co... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.2 A Conceptual Model of the UML | The UML has three major elements: - a set of basic building blocks: ETSI ETSI TR 102 105 V1.1.1 (1999-08) 12 - things; - relationships; - diagrams. - a set of rules for assembling these blocks; - some common mechanisms (for example, to extend the UML notation). |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.2.1 Building Blocks of the UML | |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.2.1.1 Things | The UML defines four different types of "thing": - structural things: the nouns of a UML model. These are the static parts of a model and they represent conceptual elements of a system. Classes, interfaces, collaborations, use cases, active classes, components and nodes are structural things in the UML; - behavioural t... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.2.1.2 Relationships | The UML supports four kinds of basic relationships as follows: - dependency: a semantic relationship between two things. A change to one thing may affect the other; - association: a structural relationship. An aggregation, which is a special kind of association, represents the relationship between a whole and its const... |
7bacf70a4dd0b8f943de5e517835b45e | 102 105 | 4.3.1.2.1.3 Diagrams | A diagram is a graphical presentation of a set of elements. UML has nine different diagrams which can be used to provide different views of a system's architecture. Diagrams are classified as either structural or behavioural diagrams. ETSI ETSI TR 102 105 V1.1.1 (1999-08) 13 4.3.1.2.1.3.1Structural diagrams A class dia... |
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