AI-Driven Intrusion Detection Systems (IDS)

Deep learning-based IDS monitors network traffic for malicious activity. IDS implementation includes:

• Signature-Based Detection: Uses pre-trained CNNs to classify known attack patterns.
• Behavioral Analysis: LSTMs analyze time-series data to detect unusual system activity.
• Hybrid IDS Models:
• Deep Belief Networks (DBNs): Improve detection accuracy by analyzing multiple data layers.
• Attention Mechanisms: Focus on critical threat indicators within log data.
• Federated Learning: Enables distributed anomaly detection without sharing raw data across networks.