Incidents Are Meant for Learning, Not Repeating: Sharing Knowledge About Security Incidents in Cyber-Physical Systems

Alrimawi, Faeq; Pasquale, Liliana; Mehta, Deepak; Yoshioka, Nobukazu and Nuseibeh, Bashar (2020). Incidents Are Meant for Learning, Not Repeating: Sharing Knowledge About Security Incidents in Cyber-Physical Systems. IEEE Transactions on Software Engineering (Early access).

DOI: https://doi.org/10.1109/TSE.2020.2981310

Abstract

Cyber-physical systems (CPSs) are part of many critical infrastructures such as industrial automation and transportation systems. Thus, security incidents targeting CPSs can have disruptive consequences to assets and people. As incidents tend to re-occur, sharing knowledge about these incidents can help organizations be more prepared to prevent, mitigate or investigate future incidents. This paper proposes a novel approach to enable representation and sharing of knowledge about CPS incidents across different organizations. To support sharing, we represent incident knowledge (incident patterns) capturing incident characteristics that can manifest again, such as incident activities or vulnerabilities exploited by offenders. Incident patterns are a more abstract representation of specific incident instances and, thus, are general enough to be applicable to various systems - different than the one in which the incident occurred. They can also avoid disclosing potentially sensitive information about an organization's assets and resources. We provide an automated technique to extract an incident pattern from a specific incident instance. To understand how an incident pattern can manifest again in other cyber-physical systems, we also provide an automated technique to instantiate incident patterns to specific systems. We demonstrate the feasibility of our approach in the application domain of smart buildings. We evaluate correctness, scalability, and performance using two substantive scenarios inspired by real-world systems and incidents.

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