Western-OC2-Lab/AutoML-and-Adversarial-Attack-Defense-for-Zero-Touch-Network-Security
This repository includes code for the AutoML-based IDS and adversarial attack defense case studies presented in the paper "Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis" published in IEEE Transactions on Network and Service Management.
This project helps network security analysts and engineers develop robust intrusion detection systems (IDS) with minimal manual effort. It takes raw network traffic data and automatically builds, optimizes, and updates machine learning models to identify cyber threats. The output is a highly effective IDS capable of adapting to new attack patterns and defending against sophisticated adversarial attacks.
Use this if you need to quickly build and maintain an effective intrusion detection system for your network, whether for static batch analysis or dynamic real-time traffic, and want to improve its resilience against cyberattacks.
Not ideal if you are looking for a pre-packaged, ready-to-deploy commercial IDS solution, or if you do not have access to network traffic data for training and evaluation.
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MIT
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Last pushed
Dec 19, 2025
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