A Few-Shot and Anti-Forgetting Network Intrusion Detection System based on Online Meta Learning
Zhen Wang, Yifei Lu, Wenxin Wu, Yun Lu, Hongxiang Wang · 2024
In the actual Internet of Things (IoT) environment, the proportion of abnormal behavior is much lower than that of normal behavior, and abnormal samples are often scarce, so it is a significant challenge to train efficient network intrusion detection systems using limited labeled samples. Meanwhile, intrusion detection systems based on online learning are prone to catastrophic forgetting, which greatly reduces the performance of online models. Previous research has not comprehensively addressed these two issues. Therefore, this paper proposes a few-shot and anti-forgetting network intrusion detection system based on online meta-learning. The system uses meta-learning as the basic algorithm to efficiently utilize data to train well-performing models with very few samples, thereby addressing the problem of insufficient samples; meanwhile, by saving previous models and invoking them when necessary to combat catastrophic forgetting. The experiments on the CIC-IDS2017 and CIC-IDS2018 datasets show that our proposed detection system can maintain good performance over the long term without forgetting.