Artificial Intelligence

Methods for testing whether TETRA Voice plus Data (V+D) Base Station (BS) and Mobile Station (MS) equipment and TETRA Direct Mode Operation (DMO) equipment achieve the performance specified in ETSI EN 300 392-2 [1]. Specific test methods for DMO equipment are recommended in annex F of the present document. The purpose of these specifications is to provide a sufficient quality of radio transmission and reception for equipment operating in a TETRA system and to minimize harmful interference to other equipment. The present document is applicable to TETRA systems operating at radio frequencies in the range of 137 MHz to 1 GHz. Versions V3.3.1 [i.5] and earlier of the present document specified the methods used for type testing. The minimum technical characteristics of TETRA Voice plus Data (V+D) Base Station (BS) and Mobile Station (MS) equipment and TETRA Direct Mode Operation (DMO) equipment and radio test methods to be used for providing presumption of conformity, are now specified in ETSI EN 303 758

The present document summarizes and analyses existing and potential mitigation against threats for AI-based systems as discussed in ETSI GR SAI 004 [i.1]. The goal is to have a technical survey for mitigating against threats introduced by adopting AI into systems. The technical survey shed light on available methods of securing AI-based systems by mitigating against known or potential security threats. It also addresses security capabilities, challenges, and limitations when adopting mitigation for AI-based systems in certain potential use cases.

The present document describes the problem of securing AI-based systems and solutions, with a focus on machine learning, and the challenges relating to confidentiality, integrity and availability at each stage of the machine learning lifecycle. It also describes some of the broader challenges of AI systems including bias, ethics and explainability. A number of different attack vectors are described, as well as several real-world use cases and attacks.

AI-specific requirements with regard to robustness, espe-cially regarding adversarial robustness and corruption robustness

Procedure for data collection, structuring of data for learn-ing AI image recognition, process structure of learning experiments and quality assurance

Assessment of AI systems: Metrics for the performance capability of AI

This document describes the framework of the big data reference architecture and the process for how a user of the document can apply it to their particular problem domain.

SO/IEC TR 20547-2:2018 provides examples of big data use cases with application domains and technical considerations derived from the contributed use cases.

ISO/IEC TR 20547-5:2018 describes big data relevant standards, both in existence and under development, along with priorities for future big data standards development based on gap analysis.

ISO/IEC 18045:2008 is a companion document to ISO/IEC 15408, Information technology - Security techniques - Evaluation criteria for IT security. ISO/IEC 18045:2008 defines the minimum actions to be performed by an evaluator in order to conduct an ISO/IEC 15408 evaluation, using the criteria and evaluation evidence defined in ISO/IEC 15408. ISO/IEC 18045:2008 does not define evaluator actions for certain high assurance ISO/IEC 15408 components, where there is as yet no generally agreed guidance.