Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats
Title | Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats |
Publication Type | Conference Paper |
Year of Publication | 2019 |
Authors | Cody Kinneer, Ryan Wagner, Fei Fang, Claire Le Goues, David Garlan |
Conference Name | 17th ACM-IEEE International Conference on Formal Methods and Models for System Design |
Date Published | 10/2019 |
Publisher | Association for Computing Machinery |
Conference Location | California |
ISBN Number | 978-1-4503-6997-8 |
Keywords | 2020: January, CMU, Human Behavior, Metrics, Model-Based Explanation For Human-in-the-Loop Security, Resilient Architectures |
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. |
URL | https://dl.acm.org/doi/10.1145/3359986.3361208 |
DOI | 10.1145/3359986.3361208 |
Citation Key | node-66320 |
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