shahanHasan/Intrusion-Detection-System-Adversarial-Attacks-
Network Intrusion Detection System on CSE-CIC-IDS2018 using ML classifiers and DNN ( ANN , CNN , RNN ) | Hyper-parameter Optimization { learning rate, epochs, network architectures, regularisation } | Adversarial Attacks - Label flip , Adversarial samples , KNN (defence)
This project helps network security analysts and operations engineers evaluate the robustness of their network intrusion detection systems (NIDS). It takes raw network traffic data and applies various machine learning and deep learning models to identify threats. The output helps understand how well a NIDS can detect common attack types and withstand sophisticated adversarial attacks, providing insights into its reliability.
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Use this if you need to test and understand how vulnerable your network intrusion detection system is to different types of cyberattacks, including 'stealthy' adversarial samples.
Not ideal if you are looking for a plug-and-play intrusion detection system to deploy directly into a production network.
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Oct 16, 2021
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