Exploring AI in Network Traffic Analysis
I’ve been diving deep into an AI-assisted approach for detecting patterns in network traffic, through my project, Phone Home Detector. This innovative tool analyzes traffic between IP address pairs, breaking it down into manageable one-minute buckets.
Key Highlights:
- Pattern Detection: Applying fixed rules based on byte counts and transmission intervals.
- Experimental Extension: Recently prototyped a tool to expose transmission data via a MCP tool, making it queryable by Large Language Models (LLMs).
- Intriguing Findings: Initial results show consistent data sizes sent to specific IPs, though intervals present variability without clear patterns.
While my approach is still experimental, I see potential for deeper insights beyond static rules. This exploration is a stepping stone for enhancing network analytics.
🔍 Curious about the intersection of AI and network traffic? Join the conversation! Feel free to share your thoughts or experiences below!
