Improving the Stability of Networks Anomaly Detection in Internet of Things

Networks Anomaly Detection is very critical to ensure the security in IoT. However, the noise in training and detection samples varies due to differences in scenarios and devices, and this different noise information corresponds to distinct false correlation relationships. This leads to a lack of stability in existing detection models based on correlation reasoning. To address these issues, in this paper, we propose a novel causal diffusion approach to detect anomalies in IoT. The model first generates independent features by adding noise to remove false correlations and subsequently addresses the problems of Not Independent and Identically Distributed (N-IID) sample distributions by calculating the causal effect relationships between the labels and features to remove noise. Finally, through the validation of one broad and representative network intrusion detection dataset, the experimental results show that method can achieve a maximum detection rate of >99% in the actual different network environments in CICIDS2019.