An efficient network intrusion detection model based on beta mixture models

With the rapid development of computer networks and network applications, ensuring network security has become a critical concern and has garnered significant attention from both academia and industry. Network intrusion detection (NID) plays a pivotal role in safeguarding cybersecurity and maintaining system stability. Most existing NID approaches rely on traditional machine learning (ML) or deep learning (DL) techniques to identify threats and potential attacks based on network traffic data. However, these methods often suffer from high computational complexity and large model sizes, which significantly impede their deployment in resource-constrained environments such as the Internet of Things (IoT), edge computing infrastructures, and wireless sensor networks. In this study, we propose an efficient NID framework based on the Beta Mixture Model (BMM) classifier. The proposed method integrates the BMM with the recently introduced Extended Stochastic Variational Inference (ESVI) framework to effectively characterize both normal and intrusive behavior patterns. The ESVI framework enables simultaneous parameter estimation and model complexity control in a principled and computationally efficient manner. Experimental evaluations show that, compared to NID methods utilizing established finite mixture models, traditional ML, or state-of-the-art DL techniques, our approach substantially reduces computational overhead while achieving comparable detection performance.