MD-CGM: Malicious traffic detection model based on CycleGAN and multi-head self-attetion mechanism

Key Points

• Malicious traffic detection accuracy reached significant levels using CycleGAN with self-attention.
• The detection model achieved 95% precision in identifying threats in network traffic.
• Analysis employed a generative adversarial network setup with multi-head self-attention for improved results.
• Findings support implementation in real-time systems, suggesting increased network security and efficiency.