Biblio

Filters: Author is Le Goues, Claire  [Clear All Filters]
2023-01-30
Kinneer, Cody, Wagner, Ryan, Fang, Fei, Le Goues, Claire, Garlan, David.  2019.  Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats. In Proceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for Systems Design (MEMCODE\'19.

Advanced persistent threats (APTs) are a particularly troubling challenge for software systems. The adversarial nature of the security domain, and APTs in particular, poses unresolved challenges to the design of self-* systems, such as how to defend against multiple types of attackers with different goals and capabilities. In this interaction, the observability of each side is an important and under-investigated issue in the self-* domain. We propose a model of APT defense that elevates observability as a first-class concern. We evaluate this model by showing how an informed approach that uses observability improves the defender's utility compared to a uniform random strategy, can enable robust planning through sensitivity analysis, and can inform observability-related architectural design decisions.