Adversarial Resilience Learning - Towards Systemic Vulnerability Analysis for Large and Complex Systems
Fischer, Lars, Memmen, Jan-Menno, Veith, Eric MSP, Tröschel, Martin
–arXiv.org Artificial Intelligence
Current newspapers are full of horrific tales of "cyber-attackers" threatening our energy systems. And, if not for the notorious "evil state"-actor, it is the ongoing digitization necessary to enable increasing renewable and volatile energy generation that threatens our energy supply and thus the stability of our society. And while the main approach seems to be to patch-up the detected vulnerabilities of protocols, software and controller devices, our approach is to research and develop the means to systematically design and test systems that are structurally resilient against failures and attackers alike. Security in cyber-systems mostly should be concerned with establishing asymetric control in favour of the operator of a system. In order to achieve this on a structural level at design time, reproducible benchmark tests are required.
arXiv.org Artificial Intelligence
Nov-15-2018
- Country:
- Europe (0.46)
- Genre:
- Research Report (0.64)
- Industry:
- Information Technology > Security & Privacy (1.00)
- Energy > Power Industry (1.00)
- Technology: