Data Contamination Defense Method for Intelligent Network Intrusion Detection Systems Based on Edge Examples

Artificial intelligence has been widely used in network intrusion detection systems. Due to the concept drift of traffic samples, the models used for malicious traffic identification must be updated frequently to adapt to new feature distributions. The effectiveness of the updated model depends on the quality of the new training samples, so it is essential to prevent data contamination. However, contamination filtering of traffic samples still relies on expert experience, which leads to the problems such as the immense workload of sample screening, unstable model accuracy, and vulnerability to poisoning attacks during the model update. Existing works cannot achieve contamination filtering or model repair while maintaining model performance. We design a general model update method for intelligent network intrusion detection systems to solve the above problems. In this paper, we first design the EdgeGAN algorithm to make the generative adversarial network fit the model edge example distribution through fuzzing. Then a subset of contaminated examples is identified by examining the MSE values of the new training samples and the original model and checking the F\-β scores of the updated model on the old edge examples. The influence of poisoned examples is suppressed by letting the model learn malicious edge examples, and the model is guaranteed to recover quickly after poisoning. Finally, the effectiveness of the update method on contamination filtering and model restoration is verified by experimental testing on 5 typical intelligent network intrusion detection systems. Compared with the state-of-the-art methods, the new method improves the detection rate of poisoned examples by 12.50% and the restoration effect of poisoned models by 6.38%. The method is applicable to protect the update process of any common intelligent network intrusion detection systems, which can reduce the manual sample screening work, effectively reduce the cost of poison detection and model repair, and provide guarantees for model performance and robustness. The new method can also protect similar intelligent threat detection models.