Visible to the public Modeling Observability in Adaptive Systems to Defend Against Advanced Persistent ThreatsConflict Detection Enabled

TitleModeling Observability in Adaptive Systems to Defend Against Advanced Persistent Threats
Publication TypeConference Paper
Year of Publication2019
AuthorsCody Kinneer, Ryan Wagner, Fei Fang, Claire Le Goues, David Garlan
Conference Name17th ACM-IEEE International Conference on Formal Methods and Models for System Design
Date Published10/2019
PublisherAssociation for Computing Machinery
Conference LocationCalifornia
ISBN Number978-1-4503-6997-8
Keywords2020: 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.

URLhttps://dl.acm.org/doi/10.1145/3359986.3361208
DOI10.1145/3359986.3361208
Citation Keynode-66320

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