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Deep Reinforcement Learning for Cyber Security.

Thanh Thi Nguyen, Vijay Janapa Reddi

    IEEE Transactions on Neural Networks and Learning Systems
    |November 1, 2021
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    This summary is machine-generated.

    Deep reinforcement learning (DRL) offers adaptive solutions for complex cyberattacks. This survey reviews DRL applications in cybersecurity, including intrusion detection and defense strategies, highlighting its potential for future cyber defense.

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    Area of Science:

    • Computer Science
    • Cybersecurity
    • Artificial Intelligence

    Background:

    • Internet-connected systems face escalating cyber threats due to their increasing scale and complexity.
    • Traditional cybersecurity measures struggle with the dynamic and high-dimensional nature of modern cyberattacks.
    • Machine learning, particularly deep reinforcement learning (DRL), presents a promising avenue for advanced cyber defense.

    Purpose of the Study:

    • To survey and analyze the application of deep reinforcement learning (DRL) methods in the field of cybersecurity.
    • To explore DRL's capability in addressing the responsiveness, adaptability, and scalability requirements for cyber defense.
    • To provide a comprehensive overview of DRL-based security techniques and identify future research directions.

    Main Methods:

    • Review of existing literature on deep reinforcement learning (DRL) applied to cybersecurity challenges.
    • Categorization of DRL approaches based on their application areas, including cyber-physical systems, intrusion detection, and defense strategy simulations.
    • Analysis of DRL's effectiveness in handling complex, dynamic, and high-dimensional cyber defense problems.

    Main Results:

    • DRL methods show significant potential for developing autonomous and adaptive cybersecurity solutions.
    • Applications span critical areas such as securing cyber-physical systems and enabling intelligent intrusion detection.
    • Multiagent DRL effectively models game-theoretic strategies for enhanced defense against sophisticated cyberattacks.

    Conclusions:

    • Deep reinforcement learning (DRL) is a powerful paradigm for tackling the evolving landscape of cyber threats.
    • This review establishes a foundation for future research into DRL-driven cybersecurity innovations.
    • Further exploration of DRL is crucial for developing robust and scalable cyber defense mechanisms.