This research introduces innovative AI models that analyze network packet content to detect cyber threats with higher accuracy than traditional methods.

• Combines Convolutional Multi-Head Attention Ensemble (CMAE) with Large Language Models to detect malicious network traffic
• Focuses on payload-aware analysis rather than just flow statistics, enabling detection of sophisticated attack patterns
• Addresses limitations of signature-based approaches, particularly for zero-day attacks
• Demonstrates lower false positive rates while maintaining high detection accuracy

This advancement matters for security teams seeking to strengthen network defenses against emerging threats while reducing alert fatigue from false positives.

Payload-Aware Intrusion Detection with CMAE and Large Language Models