NetNerve – AI-Driven PCAP Analysis for Detecting Anomalies and Threats

Our platform leverages machine learning algorithms (e.g., LSTM networks and clustering models) to process packet capture files (PCAP/CAP) without manual intervention.

The system automatically decodes TCP/IP stack layers, extracts metadata (source/destination IPs, ports via tcp.srcport/tcp.dstport), and applies entropy analysis to detect encrypted payloads.

Using YARA rules and Snort signatures (e.g., alert tcp any any -> any 80 (content:"|27|malicious|00|";), it identifies known threat patterns while anomaly detection models flag deviations from baseline network behavior.

The AI engine correlates flow records (NetFlow v9) with threat intelligence feeds for real-time analysis.

Actionable Threat Intelligence Delivery

Security teams receive prioritized alerts through STIX/TAXII formatted reports, including:

• IoC (Indicators of Compromise) visualization (e.g., malicious IP 192.0.2.0/24 flagged via ip.src == 192.0.2.*)
• Protocol distribution heatmaps highlighting abnormal HTTP POST request volumes
• Payload extraction from suspicious packets (e.g., tcp.payload contains "cmd.exe")
• TTP (Tactics, Techniques, Procedures) mapping to MITRE ATT&CK framework (e.g., T1040)
  The system generates Python scripts for automated response (e.g., scapy filters to block malicious MAC addresses) and exports findings in PCAPNG format for forensic review.

Educational and Research Applications

For cybersecurity students, the platform provides:

• Hands-on labs with sample PCAPs containing ARP spoofing (Wireshark filter: arp.opcode == 2)
• DNS exfiltration detection tutorials (identifying base64 in DNS TXT queries)
• Malware C2 traffic analysis exercises (detecting beaconing via tcp.flags.syn == 1 && tcp.flags.ack == 0)
  Researchers benefit from AI model tuning capabilities using custom TensorFlow pipelines and Zeek/Bro log integration for behavioral analysis.
• The platform’s REST API (POST /api/v1/analyze) enables batch processing for large-scale studies.

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