<ul data-eligibleForWebStory="false"><li>This paper introduces a new intrusion detection approach that combines traditional signature-based methods with the contextual understanding abilities of the GPT-2 Large Language Model (LLM).</li><li>As cyber threats in IoT networks grow more advanced, the necessity for dynamic and adaptive Intrusion Detection Systems (IDSs) is crucial.</li><li>While traditional methods are effective against known threats, they struggle to identify new and evolving attack patterns, unlike GPT-2 which excels at processing unstructured data and uncovering subtle zero-day attack vectors.</li><li>The proposed hybrid IDS framework integrates signature-based techniques with GPT-2-driven semantic analysis, showing improvements in detection accuracy, reduction in false positives, and maintaining near real-time responsiveness in experimental evaluations on an intrusion dataset.</li></ul>

Hybrid LLM-Enhanced Intrusion Detection for Zero-Day Threats in IoT Networks

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