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In the dynamic landscape of cybersecurity, the constant evolution of threats necessitates innovative approaches to fortify digital defenses. This chapter delves into a cutting-edge paradigm, exploring the integration of reinforcement learning techniques with hardware-assisted (cyber)security measures. As the relentless sophistication of cyber threats challenges conventional security mechanisms, this chapter elucidates how reinforcement learning, a form of adaptive artificial intelligence, can be harnessed to adapt and fortify defenses in real time. The convergence of hardware-assisted security and adaptive learning promises a robust and adaptive shield against emerging threats particularly malicious software, bringing in a new era of proactive and resilient cybersecurity strategies in the digital age.

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Adapt and Defend: Reinforcement Learning for Hardware-Assisted Security

  • 章节

  • 发表日期：

    2025年

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  • Hossein Sayadi,
  • Zhangying He

摘要

In the dynamic landscape of cybersecurity, the constant evolution of threats necessitates innovative approaches to fortify digital defenses. This chapter delves into a cutting-edge paradigm, exploring the integration of reinforcement learning techniques with hardware-assisted (cyber)security measures. As the relentless sophistication of cyber threats challenges conventional security mechanisms, this chapter elucidates how reinforcement learning, a form of adaptive artificial intelligence, can be harnessed to adapt and fortify defenses in real time. The convergence of hardware-assisted security and adaptive learning promises a robust and adaptive shield against emerging threats particularly malicious software, bringing in a new era of proactive and resilient cybersecurity strategies in the digital age.