oqadiSAK/fl-ids
Federated Learning for Intrusion Detection System using the Flower framework and UNSW_NB15 dataset.
This project helps network security professionals build and test an Intrusion Detection System (IDS) without centralizing sensitive network traffic data. It takes network flow data (like the UNSW_NB15 dataset) from multiple sources, learns from it collaboratively, and produces a shared, more robust threat detection model. This is for security analysts and operations engineers who need to improve their network defenses while maintaining data privacy across different organizational units or partners.
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Use this if you need to train an Intrusion Detection System model using data from various network environments, but cannot or prefer not to pool all the raw data into a single location due to privacy, compliance, or logistical reasons.
Not ideal if you have all your network traffic data centralized and are not concerned with distributed training or data privacy constraints.
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Language
Python
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Last pushed
Jan 13, 2024
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