Adaptive Distributed Intrusion Detection Using Parametric Model

Due to the increasing demands for network security, distributed intrusion detection has become a hot research topic in computer science. However, the design and maintenance of the intrusion detection system (IDS) is still a challenging task due to its dynamic, scalability, and privacy properties. In this paper, we propose a distributed IDS framework which consists of the individual and global models. Specifically, the individual model for the local unit derives from Gaussian Mixture Model based on online Adaboost algorithm, while the global model is constructed through the PSO-SVM fusion algorithm. Experimental results demonstrate that our approach can achieve a good detection performance while being trained online and consuming little traffic to communicate between local units.