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dc.contributor.advisorGuirguis, Mina
dc.contributor.authorTahsini, Alireza ( )
dc.date.accessioned2021-09-14T14:17:32Z
dc.date.available2021-09-14T14:17:32Z
dc.date.issued2019-08
dc.identifier.citationTahsini, A. (2019). BLOC: A game theoretic approach to orchestrate CPS against cyber attacks (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14494
dc.description.abstractOne important aspect in protecting CPS is ensuring that the proper control and measurement signals are propagated within the control loop. The CPS research community has been developing a large set of check blocks that can be integrated within the control loop to check signals against various types of attacks (e.g., false data injection attacks). Unfortunately, it is not possible to integrate all these "checks" within the control loop as the overhead in checking signals may violate the delay constraints dedicated by the control loop. Moreover, these blocks do not completely operate in isolation of each other, but dependencies exist among them in terms of their effectiveness against detecting a subset of attacks. Thus, it becomes a challenging and a complex problem to assign the proper checks, specially with the presence of a rational adversary who can observe the check blocks assigned and optimizes her own attack strategies accordingly. This paper tackles the inherent state-action space explosion that arise in securing CPS through developing a unifying framework in which Deep Reinforcement Learning algorithms are utilized to provide optimal/sub-optimal assignments of check blocks to signals. The framework models stochastic games between the adversary and the CPS defender and derives mixed strategies for assigning check blocks to ensure the integrity of the propagated signals, while abiding to the real-time constraints dictated by the control loop. Furthermore, the strategies obtained reflect various factors such as the aggressiveness and risk associated to the players in taking defense/attack actions. Our results show that our framework can obtain assignment strategies that outperform other strategies and heuristics.
dc.formatText
dc.format.extent43 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectDeep reinforcement learning
dc.subjectDeep learning
dc.subjectGame theory
dc.subjectMarkov game
dc.subjectCPS security
dc.subjectCyber physical systems
dc.titleBLOC: A Game Theoretic Approach to Orchestrate CPS Against Cyber Attacks
txstate.documenttypeThesis
dc.contributor.committeeMemberTešić, Jelina
dc.contributor.committeeMemberGu, Qijun
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
txstate.departmentComputer Science


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