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Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories

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arxiv 2404.01205 v2 pith:AV4YF6XI submitted 2024-04-01 eess.SY cs.CRcs.GTcs.SY

classification eess.SYcs.CRcs.GTcs.SY
keywords cyberlearningresiliencecontrolgamethreatschapterconfluence
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Cyber resilience is a complementary concept to cybersecurity, focusing on the preparation, response, and recovery from cyber threats that are challenging to prevent. Organizations increasingly face such threats in an evolving cyber threat landscape. Understanding and establishing foundations for cyber resilience provide a quantitative and systematic approach to cyber risk assessment, mitigation policy evaluation, and risk-informed defense design. A systems-scientific view toward cyber risks provides holistic and system-level solutions. This chapter starts with a systemic view toward cyber risks and presents the confluence of game theory, control theory, and learning theories, which are three major pillars for the design of cyber resilience mechanisms to counteract increasingly sophisticated and evolving threats in our networks and organizations. Game and control theoretic methods provide a set of modeling frameworks to capture the strategic and dynamic interactions between defenders and attackers. Control and learning frameworks together provide a feedback-driven mechanism that enables autonomous and adaptive responses to threats. Game and learning frameworks offer a data-driven approach to proactively reason about adversarial behaviors and resilient strategies. The confluence of the three lays the theoretical foundations for the analysis and design of cyber resilience. This chapter presents various theoretical paradigms, including dynamic asymmetric games, moving horizon control, conjectural learning, and meta-learning, as recent advances at the intersection. This chapter concludes with future directions and discussions of the role of neurosymbolic learning and the synergy between foundation models and game models in cyber resilience.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning

    cs.LG 2025-01 conditional novelty 3.0 of 10

    A knowledge-graph plus weighted imbalanced learning plus LLM multi-agent framework reports a 3-4% improvement in insider-threat capture on the CERT dataset.

  2. The Game-Theoretic Symbiosis of Trust and AI in Networked Systems

    cs.AI 2024-11 unverdicted novelty 2.0 of 10

    A survey chapter that combines trust scores, Bayesian updates, and game-theoretic models to argue that AI and trust should be managed as a strategic symbiosis for cybersecurity.

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