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001454570 001__ 1454570
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001454570 019__ $$a1369622847
001454570 020__ $$a9783031263699$$q(electronic bk.)
001454570 020__ $$a3031263693$$q(electronic bk.)
001454570 020__ $$z9783031263682
001454570 0247_ $$a10.1007/978-3-031-26369-9$$2doi
001454570 035__ $$aSP(OCoLC)1369667331
001454570 040__ $$aEBLCP$$beng$$cEBLCP$$dGW5XE$$dYDX$$dEBLCP
001454570 049__ $$aISEA
001454570 050_4 $$aQA76.9.A25
001454570 08204 $$a005.8$$223/eng/20230214
001454570 1112_ $$aGameSec (Conference)$$n(13th :$$d2022 :$$cPittsburgh, Pa. ; Online)
001454570 24510 $$aDecision and game theory for security :$$b13th International Conference, GameSec 2022, Pittsburgh, PA, USA, October 26-28, 2022, Proceedings /$$cFei Fang, Haifeng Xu, Yezekael Hayel, editors.
001454570 2463_ $$aGameSec 2022
001454570 260__ $$aCham :$$bSpringer,$$c2023.
001454570 300__ $$a1 online resource (324 p.).
001454570 4901_ $$aLecture Notes in Computer Science ;$$v13727
001454570 500__ $$a2.3 The Markov Game Model for MTD
001454570 500__ $$aIncludes author index.
001454570 5050_ $$aIntro -- Preface -- Organization -- Contents -- Deception in Security -- The Risk of Attacker Behavioral Learning: Can Attacker Fool Defender Under Uncertainty? -- 1 Introduction -- 2 Related Work -- 3 Background -- 4 Attacker Behavior Deception Under Uncertainty -- 4.1 A Polynomial-Time Deception Algorithm -- 4.2 Solution Quality Analysis -- 5 Defender Counter-Deception -- 5.1 Analyzing Attacker Deception Adaptation -- 5.2 Finding Optimal Defense Function HI Given Fixed I: Divide-and-Conquer -- 5.3 Completing the Proof of Theorem 3 -- 6 Experimental Evaluation -- 7 Summary -- References
001454570 5058_ $$aCasino Rationale: Countering Attacker Deception in Zero-Sum Stackelberg Security Games of Bounded Rationality -- 1 Introduction -- 2 Related Work -- 3 Model and Preliminaries -- 4 Bounds and Analysis on Optimal Defender Behaviors -- 5 Generalized Multi-round Game of Deception -- 5.1 Single Round, No Deception -- 5.2 Single Round, Deception -- 5.3 Multi-round Game -- 6 Conclusion -- A Proof of Lemma 3 -- References -- Cyber Deception Against Zero-Day Attacks: A Game Theoretic Approach -- 1 Introduction -- 2 Related Work -- 2.1 Cyber Deception GT -- 2.2 Games on AG -- 2.3 Zero-Day -- 3 System Model
001454570 5058_ $$a3.1 Defender Model -- 3.2 Attacker Model -- 3.3 Reward Function -- 4 Zero-Day Vulnerability Analysis -- 5 Zero-Day Mitigating Strategies -- 5.1 Impact-Based Mitigation (Alpha-Mitigation) -- 5.2 Capture-Based Mitigation (LP-Mitigation) -- 5.3 Critical Point Mitigation -- 6 Numerical Results -- 6.1 Experiment -- 6.2 Impact of Zero-Days Vulnerability -- 6.3 Mitigation -- 7 Conclusion -- References -- Planning and Learning in Dynamic Environments -- On Almost-Sure Intention Deception Planning that Exploits Imperfect Observers -- 1 Introduction -- 2 Problem Formulation -- 3 Main Results
001454570 5058_ $$a3.1 Nonrevealing Intention Deception Attack Planning Against Action-Visible Defender -- 3.2 Non-revealing Intention Deception Against Action-Invisible Defender -- 3.3 Complexity Analysis -- 4 Examples -- 4.1 Illustrative Examples -- 4.2 Intention Deception Planning Against a Security Monitoring System -- 5 Conclusion and Future Work -- A Proof of Proposition 2 and the Construction of ASW Region and ASW Strategies -- B Proof of Theorem 2 -- References -- Using Deception in Markov Game to Understand Adversarial Behaviors Through a Capture-The-Flag Environment -- 1 Introduction -- 2 Background
001454570 5058_ $$a2.1 Capture-The-Flag Setup -- 2.2 Vulnerabilities and Exploits -- 2.3 Defense Strategies and Analysis Tools -- 2.4 Attack Graph -- 2.5 Markov Game -- 3 Methodology -- 3.1 Hypotheses -- 3.2 User Study -- 3.3 Markov Game Modeling -- 4 Experimental Evaluation and Results -- 4.1 iCTF User Studies -- 4.2 Markov Game Strategy Evaluation -- 4.3 Discussion -- 5 Related Work -- 6 Conclusion and Future Work -- References -- Robust Moving Target Defense Against Unknown Attacks: A Meta-reinforcement Learning Approach -- 1 Introduction -- 2 The MTD Game Model -- 2.1 System Model -- 2.2 Threat Model
001454570 506__ $$aAccess limited to authorized users.
001454570 520__ $$aThis book constitutes the refereed proceedings of the 13th International Conference on Decision and Game Theory for Security, GameSec 2022, held in October 2022 in Pittsburgh, PA, USA. The 15 full papers presented were carefully reviewed and selected from 39 submissions. The papers are grouped thematically on: deception in security; planning and learning in dynamic environments; security games; adversarial learning and optimization; novel applications and new game models.
001454570 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed February 14, 2023).
001454570 650_0 $$aComputer security$$vCongresses.
001454570 650_0 $$aGame theory$$vCongresses.
001454570 655_0 $$aElectronic books.
001454570 7001_ $$aFang, Fei,$$d1989-
001454570 7001_ $$aXu, Haifeng.
001454570 7001_ $$aHayel, Yezekael.
001454570 77608 $$iPrint version:$$aFang, Fei$$tDecision and Game Theory for Security$$dCham : Springer International Publishing AG,c2023$$z9783031263682
001454570 830_0 $$aLecture notes in computer science ;$$v13727.
001454570 852__ $$bebk
001454570 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-26369-9$$zOnline Access$$91397441.1
001454570 909CO $$ooai:library.usi.edu:1454570$$pGLOBAL_SET
001454570 980__ $$aBIB
001454570 980__ $$aEBOOK
001454570 982__ $$aEbook
001454570 983__ $$aOnline
001454570 994__ $$a92$$bISE