AI-Based Anomaly Detection in Network Traffic

AI-powered anomaly detection identifies deviations from normal network behavior. Machine learning models analyze real-time network traffic patterns to detect potential cyber threats such as DDoS attacks, malware communication, and data exfiltration.

Key AI Techniques:

• Supervised Learning: Uses labeled datasets to classify normal vs. anomalous traffic.
• Unsupervised Learning: Detects unknown threats by identifying outliers in traffic behavior.
• Recurrent Neural Networks (RNNs): Identify sequence-based anomalies in network packets.
• Autoencoders: Compress and reconstruct traffic data to highlight suspicious deviations.

Data Sources:

• NetFlow and IPFIX data
• Packet captures (PCAP)
• Firewall and IDS/IPS logs
• DNS and HTTP request logs