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Neuro-Adaptive Intrusion Detection Systems: A Brain-Inspired ML Architecture for Autonomous Threat Hunting

##article.authors##

  • Shubh Patel Student

DOI:

https://doi.org/10.58445/rars.2483

Keywords:

ML Architecture, Autonomous Threat Hunting, NAIDS

Abstract

The rapid evolution of cyber threats, particularly those driven by artificial intelligence, has rendered traditional signature-based and rule-based intrusion detection systems (IDS) increasingly ineffective. These systems often suffer from high false-positive rates and lack the adaptability required to combat modern attack vectors. In response, this paper proposes Neuro-Adaptive Intrusion Detection Systems (NAIDS), a novel approach that integrates principles from computational neuroscience with state-of-the-art machine learning techniques. NAIDS emulates core functions of the human brain, such as synaptic plasticity, hierarchical processing, and real-time decision-making, to autonomously detect, classify, and mitigate advanced threats, including zero-day exploits. The architecture aligns seamlessly with the NIST Cybersecurity Framework (CSF) 2.0, offering a structured and adaptable defense mechanism that continuously learns and evolves in dynamic cyber environments.

References

NIST. (2025). Cybersecurity Framework Version 2.0. NIST SP 800-61r3. https://www.nist.gov/cyberframework

Davies, M. et al. (2021). Advancing neuromorphic computing with Loihi 2. IEEE Micro. https://doi.org/10.1109/MM.2021.3069424

Goodfellow, I., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. ICLR. https://arxiv.org/abs/1412.6572

Abadi, M. et al. (2016). Deep learning with differential privacy. CCS 2016. https://doi.org/10.1145/2976749.2978318

Samek, W. et al. (2017). Explainable AI: Understanding and visualizing deep learning. Pattern Recognition. https://doi.org/10.1016/j.patcog.2017.10.005

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Posted

2025-04-19

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Copyright (c) 2025 Shubh Patel

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