计算机科学 ›› 2018, Vol. 45 ›› Issue (2): 231-235.doi: 10.11896/j.issn.1002-137X.2018.02.040
马占飞,陈虎年,杨晋,李学宝,边琦
MA Zhan-fei, CHEN Hu-nian, YANG Jin, LI Xue-bao and BIAN Qi
摘要： 网络入侵检测一直是计算机网络安全领域的研究热点,当前网络面临着诸多的安全隐患。为了提高网络入侵检测的准确性,首先对粒子群优化(Particle Swarm Optimization,PSO)算法进行了改进,然后利用改进的PSO算法(IPSO算法)对支持向量机(Support Vector Machine,SVM)的参数进行了优化,并在此基础上设计了一种新型的基于IPSO-SVM算法的网络入侵检测方法。实验结果表明,相比于经典的SVM和PSO-SVM算法,IPSO-SVM算法不仅 明显改善了网络训练的收敛速度,而且其网络入侵检测的正确率分别提高了7.78%和4.74%,误报率分别降低了3.37%和1.19%,漏报率分别降低了1.46%和0.66%。
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