关键词: 网络入侵检测, 特征选择, 量子进化算法, Fisher比

Abstract: Concerning the disadvantages of slow detection speed in current network intrusion detection, a new feature selection method of network intrusion detection was put forward. The method applied Quantum Evolutionary Algorithm (QEA) to feature selection of network intrusion detection, extracted an optimal subset used in intrusion detection from the original feature set in network connections, so as to get better detection efficiency. First, QEA was improved in order to make its searching performance better, and the criterion function of feature subset was constructed based on the Fisher ratio of feature attributes. Then, the feature selection algorithm of network intrusion detection was designed according to QEA flow. Last, experiments were carried out using the sample data from KDD99. The experimental results show that the proposed algorithm is effective, and it can not only ensure the classification performance of intrusion detection but also improve the detection efficiency.

Key words: network intrusion detection, feature selection, Quantum Evolutionary Algorithm (QEA), Fisher ratio