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

• URL: http://arxiv.org/abs/2404.01205v2
• Date: Fri, 5 Apr 2024 14:16:33 GMT
• Title: Foundations of Cyber Resilience: The Confluence of Game, Control, and Learning Theories
• Authors: Quanyan Zhu,
• Abstract summary: Cyber resilience focuses on preparation, response, and recovery from cyber threats that are challenging to prevent. Game theory, control theory, and learning theories are three major pillars for the design of cyber resilience mechanisms. This chapter presents various theoretical paradigms, including dynamic asymmetric games, moving horizon control, conjectural learning, and meta-learning.
• Score: 15.764094200832071
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Threat analysis and adversarial model for Smart Grids [1.7482569079741024]
  The cyber domain of this smart power grid opens a new plethora of threats. Different stakeholders including regulation bodies, industry and academy are making efforts to provide security mechanisms to mitigate and reduce cyber-risks. Recent work shows a lack of agreement among grid practitioners and academic experts on the feasibility and consequences of academic-proposed threats. This is in part due to inadequate simulation models which do not evaluate threats based on attackers full capabilities and goals.
  arXiv  Detail & Related papers  (2024-06-17T16:33:46Z)
• Siren -- Advancing Cybersecurity through Deception and Adaptive Analysis [0.0]
  This project employs sophisticated methods to lure potential threats into controlled environments. The architectural framework includes a link monitoring proxy, a purpose-built machine learning model for dynamic link analysis. The incorporation of simulated user activity extends the system's capacity to capture and learn from potential attackers.
  arXiv  Detail & Related papers  (2024-06-10T12:47:49Z)
• Symbiotic Game and Foundation Models for Cyber Deception Operations in Strategic Cyber Warfare [16.378537388284027]
  We are currently facing unprecedented cyber warfare with the rapid evolution of tactics, increasing asymmetry of intelligence, and the growing accessibility of hacking tools. This chapter aims to highlight the pivotal role of game-theoretic models and foundation models (FMs) in analyzing, designing, and implementing cyber deception tactics.
  arXiv  Detail & Related papers  (2024-03-14T20:17:57Z)
• Disentangling the Causes of Plasticity Loss in Neural Networks [55.23250269007988]
  We show that loss of plasticity can be decomposed into multiple independent mechanisms. We show that a combination of layer normalization and weight decay is highly effective at maintaining plasticity in a variety of synthetic nonstationary learning tasks.
  arXiv  Detail & Related papers  (2024-02-29T00:02:33Z)
• Causal Reasoning: Charting a Revolutionary Course for Next-Generation AI-Native Wireless Networks [63.246437631458356]
  Next-generation wireless networks (e.g., 6G) will be artificial intelligence (AI)-native. This article introduces a novel framework for building AI-native wireless networks; grounded in the emerging field of causal reasoning. We highlight several wireless networking challenges that can be addressed by causal discovery and representation.
  arXiv  Detail & Related papers  (2023-09-23T00:05:39Z)
• A Survey of Network Requirements for Enabling Effective Cyber Deception [0.0]
  This paper investigates the crucial network requirements essential for the successful implementation of effective cyber deception techniques. With a focus on diverse network architectures and topologies, we delve into the intricate relationship between network characteristics and the deployment of deception mechanisms.
  arXiv  Detail & Related papers  (2023-09-01T00:38:57Z)
• Designing an attack-defense game: how to increase robustness of financial transaction models via a competition [69.08339915577206]
  Given the escalating risks of malicious attacks in the finance sector, understanding adversarial strategies and robust defense mechanisms for machine learning models is critical. We aim to investigate the current state and dynamics of adversarial attacks and defenses for neural network models that use sequential financial data as the input. We have designed a competition that allows realistic and detailed investigation of problems in modern financial transaction data. The participants compete directly against each other, so possible attacks and defenses are examined in close-to-real-life conditions.
  arXiv  Detail & Related papers  (2023-08-22T12:53:09Z)
• Graph Mining for Cybersecurity: A Survey [61.505995908021525]
  The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-world cyber entities. With the proliferation of graph mining techniques, many researchers investigated these techniques for capturing correlations between cyber entities and achieving high performance.
  arXiv  Detail & Related papers  (2023-04-02T08:43:03Z)
• Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey [114.17568992164303]
  Adrial attacks and defenses in machine learning and deep neural network have been gaining significant attention. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks.
  arXiv  Detail & Related papers  (2023-03-11T04:19:31Z)
• Reinforcement Learning for Feedback-Enabled Cyber Resilience [24.92055101652206]
  Cyber resilience provides a new security paradigm that complements inadequate protection with resilience mechanisms. A Cyber-Resilient Mechanism ( CRM) adapts to the known or zero-day threats and uncertainties in real-time. We review the literature on RL for cyber resiliency and discuss the cyber-resilient defenses against three major types of vulnerabilities.
  arXiv  Detail & Related papers  (2021-07-02T01:08:45Z)
• Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain [58.30296637276011]
  This paper summarizes the latest research on adversarial attacks against security solutions based on machine learning techniques. It is the first to discuss the unique challenges of implementing end-to-end adversarial attacks in the cyber security domain.
  arXiv  Detail & Related papers  (2020-07-05T18:22:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.