AI-POWERED INTRUSION DETECTION SYSTEM: A NEXT-GENERATION APPROACH TO CYBERSECURITY
Keywords:
Artificial Intelligence (AI), Network Intrusion Detection Systems (IDS), Deep Learning, AI-driven Solutions, Detection AccuracyAbstract
This paper provides an in-depth analysis of artificial intelligence techniques employed in network intrusion detection systems (IDS). We describe how the techniques have evolved from traditional signature-based systems to emerging AI-based solutions, with emphasis on deep learning, federated learning, and reinforcement learning. Experiments on benchmark datasets demonstrate that AI-based solutions perform better than traditional methods in correctly detecting, minimizing false positives, and learning new attacks. Nevertheless, there are still significant challenges such as comprehending how the model functions, defending against attacks, and requiring more computing power. We conclude by proposing potential areas for future work, such as explainable AI, integrating various data types, and automatic response systems.
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