A framework for constructing features and models for intrusion detection systems
ACM Transactions on Information and System Security (TISSEC)
Winning the KDD99 classification cup: bagged boosting
ACM SIGKDD Explorations Newsletter
Fast Distributed Outlier Detection in Mixed-Attribute Data Sets
Data Mining and Knowledge Discovery
An adaptive intrusion detection algorithm based on clustering and kernel-method
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Hierarchical Kohonenen net for anomaly detection in network security
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
AdaBoost-Based Algorithm for Network Intrusion Detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Visual Tracker Using Sequential Bayesian Learning: Discriminative, Generative, and Hybrid
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Minimal complexity attack classification intrusion detection system
Applied Soft Computing
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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.