AI-Powered Intrusion Detection Systems: A Hybrid Model for Adaptive Cybersecurity
Abstract
Intrusion Detection Systems (IDS) are critical for monitoring and safeguarding networks from unauthorized access. This paper introduces a hybrid AI model that combines signature-based and anomaly-based techniques to create a more adaptive IDS. By learning from past intrusions and continuously adjusting to new threats, the system enhances its accuracy and detection speed. Testing on diverse network environments shows that the hybrid model outperforms traditional IDS in detecting a wide range of intrusions, making it a robust solution for dynamic cybersecurity needs.
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