Game Theory for Security
A special issue of Games (ISSN 2073-4336).
Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 17091
Special Issue Editors
Interests: game theory for cybersecurity, resilient and secure cyber-physical systems and Internet of Things, economics of security, blockchains and smart contracts, AI/ML for security
Special Issue Information
Dear Colleagues,
Cybersecurity has become a critical issue for both organizations and individual users, prompting them to employ a variety of cyber-defense solutions. As the complexity of these solutions grows with the increasing number of ofensive techniques, so does the need for rigorously explaining and improving cybersecurity decision-making. Since security decisions must consider a complex ecosystem with a number of other decision-makers, which may include adversaries as well as other defenders, they are modeled most naturally using game theory. Indeed, game theory for security has recently emerged as a new area of research that studies the conflict and cooperation between cybersecurity decision-makers.
Game theory for security has grown to be a diverse area of research. Prior work has considered models ranging from two-player games to n-player games, with the players representing various combinations of defenders and attackers. Researchers have studied many solution concepts, such as the popular Stackelberg equilibrium, and they have considered both descriptive and normative interpretations of the outcomes. Results range from characterization of best-responses and equilibria to computational results, behavioral user studies, simulations, and real-world deployments. Despite the abundance of prior work, game theory for security remains a very active research area with a plethora of open problems, driven by the practical need to understand and improve security decision-making.
Dr. Manos Panaousis
Dr. Aron Laszka
Guest Editors
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Keywords
- Security and privacy of wireless and mobile communications
- Economics of cybersecurity
- Security and privacy for the Internet-of-Things, 5G, cyberphysical systems, and cloud computing
- Risk assessment and security risk management
- Machine learning for intrusion detection
- Adversarial machine learning
- Defending against Advanced Persistent Threats
- Data trustworthiness in mobile crowdsensing
- Blockchain technologies
- Defending against social-engineering attacks
- Defending against DDoS
- Physical security
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