Three months ago, the author struggled with packet analysis during a security incident, realizing the complexity of interpreting Wireshark dumps. This inspired the creation of NetNerve, a tool that utilizes AI (LLaMA-3 via Groq) to simplify network data analysis. Instead of deciphering technical details, users can upload .pcap files and receive clear, actionable insights about security threats and anomalies in plain English. The frontend is built with Next.js 14 and TypeScript, while the backend employs FastAPI and Python/Scapy for packet processing. Key challenges included ensuring reliable parsing of real-world .pcap files and improving processing speed; switching from ChatGPT to Groq’s LLaMA-3 reduced analysis times significantly. The tool aims to aid security teams, making findings easier to convey to non-technical stakeholders. The author invites feedback for further improvements and emphasizes a commitment to making network security more accessible.
