Analyzing Practical Effectiveness of Neural NIDS solutions: Reproducing “Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems”
Members: Samuel Englert, Pinaki Mohanty, Ronald Seoh, Akshaj Uppala, and Han Zhu
In this project, we examined practical effectiveness and applicability of deep learning-based network intrusion detection systems (NIDS). While there have been significant advances in neural NIDS lately, it is yet unclear how they achieve superiority over previous approaches such as signature-based NIDS. More specifically, we need more insights into their potential drawbacks, and how well the method could potentially fit into real-life networking scenarios. Hence, we chose the state-of-the-art neural NIDS model and the evaluation results from Hashemi and Keller 2020 to conduct our analysis: Hashemi and Keller introduced the Reconstruction from Partial Observation (RePO) technique of leve