The present document is a choice of Test Purposes Description Language (TPDL), with the intention to capture all of the information required by the Test Template and should be parseable using software.
The Testing Framework (document format) specifies a testing framework defining a methodology for the development of the test strategies, test systems and resulting test specifications. The present document identifies the implementation under test (scope of the testing), the format for the test specification, the test architecture, the points of control and observation, the naming conventions (e.g. for test case ID and test case grouping ID), etc. It also provides the Implementation Conformance Statement which is basically a checklist for a client-owner so they know what parts of the specification will be tested and if any is optional. The ICS will be published as a separate GS.
The present document establishes the minimum RF characteristics and minimum performance requirements for E-UTRA User Equipment (UE).
The document defines the management of the mobile edge system, mobile edge hosts and mobile edge platforms. This includes platform configuration, performance and fault management, application monitoring, remote service configuration and service control, information gathering regarding the platform features, available services, and available virtualised resources.
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.
