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@InProceedings{Kinneer:2019:observability, AUTHOR = {Kinneer, Cody and Wagner, Ryan and Fang, Fei and Le Goues, Claire and Garlan, David}, TITLE = {Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats}, YEAR = {2019}, MONTH = {9-11 October}, BOOKTITLE = {Proceedings of the 17th ACM-IEEE International Conference on Formal Methods and Models for Systems Design (MEMCODE\'19)}, ADDRESS = {San Diego, USA}, PDF = {http://acme.able.cs.cmu.edu/pubs/uploads/pdf/memocode2019.pdf}, ABSTRACT = {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.}, KEYWORDS = {Science of Security, Self-adaptation} }