2. Control, Game, and Learning Theory for Security and Privacy

Organizers: Tamer Basar, Quanyan Zhu

Location: Peony Junior 4511

Website: https://sites.google.com/nyu.edu/cdc2023workshop/home

Lecture Schedules: https://sites.google.com/nyu.edu/cdc2023workshop/schedule

Abstract: In today's increasingly connected world, cybersecurity has emerged as a major challenge due to the ubiquitous digitalization affecting every aspect of society, life, and work. Traditional approaches to network security, such as cryptography, firewalls, and intrusion detection systems, are no longer sufficient to guarantee the security of the network as attackers become more sophisticated. Therefore, there is an urgent need to shift to a new security paradigm that takes into account the strategic behaviors and constraints on attack-and-defense resources.

Control and game theories are mathematical sciences that study dynamical feedback systems and strategic interactions among rational decision-makers. They have emerged as promising frameworks for the analysis and design of system security. Over the past few years, control and game theories been successfully applied to various security domains, including wireless community, cloud computing, industrial control systems, Internet of Things, and national homeland security. This workshop aims to discuss the recent advances in the field and bring together experts from different communities to address the challenges of cybersecurity.

The workshop program features invited presentations that cover a diverse range of applications of game theory to security issues in cyber-physical systems, computer networks, and machine learning. A particular emphasis is placed on the intersection of machine learning and game theory for cybersecurity, an area that has garnered significant attention in recent years. The intersection enables systems to automate security solutions and adapt and learn from new data, making them better suited to address dynamic and evolving security threats. By connecting game theory, control theory, and learning theory, the workshop aims to bridge the gap between theory and application, providing a powerful set of tools that can improve the effectiveness and efficiency of security applications.

The workshop aims to create a platform for the discussion of the theoretical foundations of security games. It provides a forum to discuss new modeling frameworks, analytical methods, and algorithmic solutions that bridge cognitive science, decision and control theory, data science, and network science to solidify the foundations of security games. This workshop will be supported by the IEEE CSS Technical Committee on Security and Privacy to reach out to members of the control systems community and other research communities, including communications, machine learning, and computer scientists. It is crucial to bring together experts from different communities and foster discussions to create a community and overcome the fragmentation of previous work. Through this workshop, experts aim to pave the way for more robust and effective security solutions in the future. The topics of this workshop include:

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