2.3.1 Institutional Knowledge Sharing-Security and Privacy Advice -- 2.3.2 Building Trust in a Mutating Group Surrounded by Uncertainty -- 2.3.3 Support From Abroad -- 2.4 Conclusions-Needs and Technology -- References -- 3 Forensic Proof and Criminal Liability for Development, Distribution and Use of Artificial Intelligence -- 3.1 Artificial Intelligence and Criminal Liability -- 3.2 Injuries as Crimes, from Loss of Life to Loss of Liberties, and Policies of Review -- 3.3 Investigation, AI Forensics and Proof of Responsibility -- 3.4 The Ethics of Artificial Intelligence -- 3.5 Conclusion -- References -- Part II AI/ML for CPS -- 4 Automotive Batteries as Anomaly Detectors -- 4.1 Introduction -- 4.2 Prototpe and Data Collection -- 4.3 Case-Study: Detecting Engine Anomalies Using Batteries -- 4.3.1 Automotive Battery and Vehicle Engine -- 4.3.2 Detecting RPM Anomalies with Battery -- 4.3.2.1 Data Preparation -- 4.3.2.2 Norm Model Construction -- 4.3.2.3 Anomaly Detection -- 4.3.2.4 Anomaly Verification -- 4.4 Detecting Vehicle Anomaliues Beyond Enginen RPM -- 4.5 Evaluations -- 4.5.1 B-Diag Against ``True'' Anomalies -- 4.5.1.1 Methodology -- 4.5.1.2 Evaluation Results -- 4.5.1.3 Adapter Faults or Vehicle Faults? -- 4.5.2 B-Diag Against Emulated Anomalies -- 4.5.2.1 Anomaly Model -- 4.5.2.2 Evaluation with Subaru Crosstrek -- 4.5.2.3 Evaluation with Other Vehicles -- 4.5.2.4 Diagnosing Beyond Engine RPM -- 4.6 Conclusions -- References -- 5 Zero Trust Architecture For Cyber-Physical Power System Security Based on Machine Learning -- 5.1 Introduction -- 5.2 Overview of Cyber-Physical Power System Security -- 5.2.1 The Hierarchical Structure for Cyber-Physical Power System -- 5.2.2 Cyber-Physical Power System Security -- 5.2.3 Examples for Cross-layer Failures in CPPS -- 5.3 Machine Learning Application in Cyber-Physical Power System Security. |