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Collaborative quantum optimization network intrusion detection research

机译:协同量子优化网络入侵检测研究

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摘要

In order to improve network intrusion detection rate, a cooperative quantum PSO and LS-SVM network intrusion detection model (CQPSO-LSSVM) was proposed in this paper. Network feature subset is encoded into quantum particle positions, intrusion detection accuracy is used as the evaluation criteria of a subset feature merits, a synergistic quantum particle swarm algorithm are used to find the optimal feature subset, LS-SVM is used to establish a network intrusion detection model, and KDD CUP 99 dataset is used to simulation test. The results show that, compared with other models, the proposed algorithm has improved detection efficiency and the detection rate of the network intrusion.
机译:为了提高网络入侵检测率,提出了一种协同量子PSO和LS-SVM网络入侵检测模型(CQPSO-LSSVM)。将网络特征子集编码到量子粒子位置,以入侵检测精度作为子集特征优劣的评价标准,使用协同量子粒子群算法找到最优特征子集,使用LS-SVM建立网络入侵检测模型,并使用KDD CUP 99数据集进行模拟测试。结果表明,与其他模型相比,该算法具有更高的检测效率和网络入侵检测率。

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