计算机科学 ›› 2021, Vol. 48 ›› Issue (11A): 705-712.doi: 10.11896/jsjkx.201100101
郇文明, 林海涛
HUAN Wen-ming, LIN Hai-tao
摘要: 入侵检测系统作为防火墙之后的第二道防线已经在网络安全领域得到了广泛应用,基于机器学习的入侵检测系统因其优越的检测性能吸引了越来越多的关注。为了提高入侵检测系统在多类非平衡数据中的检测性能,文中提出基于采样集成算法(OSEC)的入侵检测系统。OSEC首先根据“一对多”原则将多类别检测问题转化为多个二分类问题,然后在每个二分类问题中根据AUC值选择最优的采样集成算法以缓解数据的非平衡问题,最后根据文中设计的类别判决模块判断待测样本的具体类别。在NSL-KDD数据集上进行仿真验证,发现本系统相较于传统方法在R2L,U2R上的F1得分分别提高了0.595和0.185;对比最新的入侵检测系统,所提方法在整体检测准确率上提高了1.4%。
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