An Initial Study of Targeted Personality Models in the FlipIt Game

Basak, Anjon; Černý, Jakub; Gutierrez, Marcus; Curtis, Shelby R.; Kamhoua, Charles A.; Jones, Daniel N.; Bošanský, Branislav; Kiekintveld, Christopher

doi:10.1007/978-3-030-01554-1_36

Cited by 17 publications

(6 citation statements)

References 17 publications

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“…While BSMGs allow us to represent uncertainty over attacker types, existing formulations consider single follower types, thereby boiling down to an MG. Although we will use an MG for this scenario, we note that our framework is capable of incorporating ongoing research on the characterizing attacker types in the context of cyber-systems [28].…”

Section: Modeling Moving Target Defense Scenarios As Bsmgsmentioning

confidence: 99%

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

Sengupta,

Kambhampati

2020

Preprint

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The field of cybersecurity has mostly been a cat-and-mouse game with the discovery of new attacks leading the way. To take away an attacker's advantage of reconnaissance, researchers have proposed proactive defense methods such as Moving Target Defense (MTD). To find good movement strategies, researchers have modeled MTD as leader-follower games between the defender and a cyberadversary. We argue that existing models are inadequate in sequential settings when there is incomplete information about a rational adversary and yield sub-optimal movement strategies. Further, while there exists an array of work on learning defense policies in sequential settings for cyber-security, they are either unpopular due to scalability issues arising out of incomplete information or tend to ignore the strategic nature of the adversary simplifying the scenario to use single-agent reinforcement learning techniques. To address these concerns, we propose (1) a unifying game-theoretic model, called the Bayesian Stackelberg Markov Games (BSMGs), that can model uncertainty over attacker types and the nuances of an MTD system and (2) a Bayesian Strong Stackelberg Q-learning (BSS-Q) approach that can, via interaction, learn the optimal movement policy for BSMGs within a reasonable time. We situate BSMGs in the landscape of incomplete-information Markov games and characterize the notion of Strong Stackelberg Equilibrium (SSE) in them. We show that our learning approach converges to an SSE of a BSMG and then highlight that the learned movement policy (1) improves the state-of-the-art in MTD for web-application security and (2) converges to an optimal policy in MTD domains with incomplete information about adversaries even when prior information about rewards and transitions is absent.Preprint. Under review.

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Section: Modeling Moving Target Defense Scenarios As Bsmgsmentioning

confidence: 99%

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

Sengupta,

Kambhampati

2020

Preprint

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“…The game analysis provides a risk assessment of the system and the development of defense strategies. The applications of FlipIt games have been extended to many applications including cloud computing [20,91,137], cybercrime [6,11], and IoT systems [21,87].…”

Section: Multi-stage and Multi-phase Gamesmentioning

confidence: 99%

Cybersecurity in Robotics: Challenges, Quantitative Modeling, and Practice

Zhu¹,

Raß²,

Dieber³

et al. 2021

Preprint

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Robotics is becoming more and more ubiquitous, but the pressure to bring systems to market occasionally goes at the cost of neglecting security mechanisms during the development, deployment or while in production. As a result, contemporary robotic systems are vulnerable to diverse attack patterns, and an a posteriori hardening is at least challenging, if not impossible at all. This book aims to stipulate the inclusion of security in robotics from the earliest design phases onward and with a special focus on the cost-benefit tradeoff that can otherwise be an inhibitor for the fast development of affordable systems. We advocate quantitative methods of security management and design, covering vulnerability scoring systems tailored to robotic systems, and accounting for the highly distributed nature of robots as an interplay of potentially very many components. A powerful quantitative approach to model-based security is offered by game theory, providing a rich spectrum of techniques to optimize security against various kinds of attacks. Such a multiperspective view on security is necessary to address the heterogeneity and complexity of robotic systems. This book is intended as an accessible starter for the theoretician and practitioner working in the field.

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“…Most of these studies originate from speculative actions and responses to questions. Participants are typically drawn from Amazon Mechanical Turk (AMT), in which the target population does not possess technical or cybersecurity training and qualifications [1][2][3]. The resulting datasets and models built from these studies fail to capture interactions of attackers and defenders with real systems and networks.…”

Section: Introductionmentioning

confidence: 99%

Cybersecurity Methodology for Specialized Behavior Analysis

Padilla

Acosta

Kiekintveld

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Analyzing attacker behavior and generating realistic models to accurately capture the realities of cybersecurity threats is a very challenging task for researchers. Psychological personality and profiling studies provide a broad understanding of personality traits, but lack a level of interactive immersion that enables observers to collect concrete cybersecurity-relevant behavioral data. Participant's intricate actions and interactions with real computer systems are seldom captured in any cybersecurity studies. Our work focuses on capturing human actions and decisions to provide an empirical basis for these types of models. We provide a practical methodology that helps bridge the gap between theory and practice by facilitating construction, experimentation, and data collection for repeatable and scalable human experimentation with realistic cybersecurity scenarios. While our methodology is platform agnostic, we describe state of the art technologies that may be used to satisfy the objectives of each of the stages of the methodology.

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An Initial Study of Targeted Personality Models in the FlipIt Game

Cited by 17 publications

References 17 publications

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense

Cybersecurity in Robotics: Challenges, Quantitative Modeling, and Practice

Cybersecurity Methodology for Specialized Behavior Analysis

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