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Applying security as a lifecycle practice is becoming increasingly important to combat targeted attacks in safety-critical systems. Among others there are two significant challenges in this area: (1) the need for models that can characterize a realistic system in the absence of an implementation and (2) an automated way to associate attack vector information; that is, historical data, to such system models. We propose the cybersecurity body of knowledge (CYBOK), which takes in sufficiently characteristic models of systems and acts as a search engine for potential attack vectors. CYBOK is fundamentally an algorithmic approach to vulnerability exploration, which is a significant extension to the body of knowledge it builds upon. By using CYBOK, security analysts and system designers can work together to assess the overall security posture of systems early in their lifecycle, during major design decisions and before final product designs. Consequently, assisting in applying security earlier and throughout the systems lifecycle.
Growing amount of hydraulic fracturing (HF) jobs in the recent two decades resulted in a significant amount of measured data available for development of predictive models via machine learning (ML). In multistage fractured completions, post-fracturin
An unobservable false data injection (FDI) attack on AC state estimation (SE) is introduced and its consequences on the physical system are studied. With a focus on understanding the physical consequences of FDI attacks, a bi-level optimization probl
This paper focuses on the problem of cyber attacks for discrete event systems under supervisory control. In more detail, the goal of the supervisor, who has a partial observation of the system evolution, is that of preventing the system from reaching
In this paper, we introduce a robust sensor design framework to provide persuasion-based defense in stochastic control systems against an unknown type attacker with a control objective exclusive to its type. For effective control, such an attackers a
State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measureme