ole-knf/A-bidirectional-GPT-approach-for-detecting-malicious-network-traffic
This approach of Intrusion Detection uses two GPT models, which are trained on normal network traffic, to predict sequences of communication patterns and thereby score network packets.
This project helps network security analysts detect unusual or malicious activity by analyzing network traffic. It takes raw network packet capture files (PCAP) as input and uses two AI models to score each packet's likelihood of being normal. The output includes real-time alerts for suspicious packets and a detailed plot showing scores over time, highlighting potential attacks. Network security specialists, incident responders, or operations engineers responsible for monitoring industrial control systems would use this tool.
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Use this if you need to identify subtle anomalies in network communication patterns that might indicate a cyber-physical attack, especially within critical infrastructure like water distribution systems.
Not ideal if you need a pre-trained, ready-to-deploy intrusion detection system for general enterprise IT networks, as this project requires training with specific normal traffic data.
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Python
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Last pushed
Oct 03, 2023
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