关键词: 网络入侵, 支持向量机, 蚁群算法, 检测

Abstract: In order to improve network intrusion detection accuracy, this paper proposes a network detection method based on improved Ant Colony Optimization algorithm（ACO） and Support Vector Machine（ACO-SVM）. The parameters of SVM model are considered as the position vector of ants. Target individuals which lead the ant colony to do global rapid search are determined by dynamic and stochastic extraction, and the optimal ant of this generation searches in small step nearly. The optimal parameter value is obtained by ACO. The network intrusion detection model is obtained. The ACO-SVM performance is tested by KDD CUP99 data. The results show that the proposed method has improved the network anomaly detection accuracy, and reduced the false alarm rate.

Key words: network intrusion, Support Vector Machine（SVM）, Ant Colony Optimization（ACO） algorithm, detection