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11th Conference on Decision and Game Theory for Security (GameSec 2020)

Modern societies are becoming dependent on information, automation, and communication technologies more than ever. Managing the security of the emerging systems, many of them safety critical, poses significant challenges. The 11th Conference on Decision and Game Theory for Security (GameSec 2020) focuses on protection of heterogeneous, large-scale and dynamic cyber-physical systems as well as managing security risks faced by critical infrastructures through rigorous and practically-relevant analytical methods. GameSec 2020 invites novel, high-quality theoretical and practically-relevant contributions, which apply decision and game theory, as well as related techniques such as optimization, machine learning, dynamic control and mechanism design, to build resilient, secure, and dependable networked systems. The goal of GameSec 2020 is to bring together academic and industrial researchers in an effort to identify and discuss the major technical challenges and recent results that highlight the connections between game theory, control, distributed optimization, machine learning, economic incentives and real-world security, reputation, trust and privacy problems.

Conference Topics include (but are not restricted to):

GameSec solicits research papers, which report original results and have neither been published nor submitted for publication elsewhere, on the following and other closely related topics:

  • Game theory, control, and mechanism design for security and privacy
  • Decision making for cybersecurity and security requirements engineering
  • Security and privacy for the Internet-of-Things, cyber-physical systems, cloud computing, resilient control systems, and critical infrastructure
  • Pricing, economic incentives, security investments, and cyber insurance for dependable and secure systems
  • Risk assessment and security risk management
  • Security and privacy of wireless and mobile communications, including user location privacy
  • Socio-technological and behavioral approaches to security
  • Empirical and experimental studies with game, control, or optimization theory-based analysis for security and privacy
  • Adversarial Machine Learning and the role of AI in system security
  • Modeling and analysis of deception and antagonistic intrusion of information flow within a game-theoretic framework

Special Track on Machine Learning and Cyber Security

Machine learning provides a set of useful analytic and decision-making tools for a wide range of applications. Security research aims to address the issue of protecting networks from adversarial behaviors. The confluences between the two are increasingly important as we witness recent advances in adversarial machine learning and machine learning for security big data processing. This special track invites submissions on various data-centric models and approaches. For submissions, please select the track "Machine Learning and Cyber Security" during the submission.

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