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

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.