计算机科学 ›› 2021, Vol. 48 ›› Issue (12): 357-363.doi: 10.11896/jsjkx.201000086
• 信息安全 • 上一篇
石琳姗1, 马创2, 杨云3, 靳敏1
SHI Lin-shan1, MA Chuang2, YANG Yun3, JIN Min1
摘要： 针对物联网环境下产生的新型网络攻击的数量持续上升和复杂性不断升高,传统的异常检测算法误报率高、检测率低以及数据量大而造成计算困难等问题,提出了一种基于子空间聚类(Subspace Clustering,SSC)和BP神经网络相结合的异常检测算法。首先在网络数据集上通过子空间聚类算法中最常用的CLIQUE算法得到不同的子空间;其次对不同子空间中的数据进行BP神经网络异常检测,计算预测误差值,通过与预先设定好的精度进行比较,来不断更新阈值进行修正,以达到提高识别网络攻击的能力。仿真实验采用NSL-KDD公开数据集和物联网环境下的网络攻击数据集,将NSL-KDD公开数据集分割为4种单一攻击子集和1种混合攻击子集,通过与K-means,DBSCAN,SSC-EA以及k-KNN异常检测模型进行比较,在混合攻击子集中,SSC-BP神经网络模型的检测率比传统的K-means模型的检测率提高了6%,误检率降低了0.2%;而在4种单一攻击子集中,SSC-BP神经网络模型都能以最低的误检率检测出最多的受到攻击的网络。在物联网环境下的网络攻击数据集上,SSC-BP神经网络模型的性能均优于其他几种对比模型。
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