Adaptive deep learning for network intrusion detection by risk analysis

With increasing connectedness, network intrusion has become a critical security concern for modern information systems. The state-of-the-art performance of Network Intrusion Detection (NID) has been achieved by deep learning. Unfortunately, NID remains very challenging, and deep models may still mislabel many activities in real networks. Therefore, there is a need for risk analysis, which aims to know which activities may be mislabeled and why.