Abstract:

The rapid increase in sophisticated cyber threats necessitates the evo lution of Intrusion Detection Systems (IDS) to ensure robust network security. Traditional IDS often struggle with high false positive and negative rates, and fail to detect complex intrusion patterns. This study investigates the integration of deep learning algorithms with autoencoders to address these challenges, pro posing a novel approach to IDS enhancement. Deep learning algorithms are im plemented and optimized to improve detection accuracy and reduce false alerts. Additionally, autoencoders are utilized for feature extraction and dimensionality reduction, enhancing the IDS’s ability to identify subtle and intricate anomalies.

Description:

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