基于特征选择的物联网轻量级入侵检测方法

Lightweight IoT Intrusion Detection Method Based on Feature Selection

随着物联网的大规模使用,其安全问题也日益严峻,如何在资源有限的物联网环境中准确实时检测网络攻击是亟需解决的关键问题。基于流量特征的入侵检测系统是物联网安全的一种解决方案,但该方案存在流量特征数量繁多、不利于训练快速轻量的检测模型的问题。针对该问题,文章提出一种基于特征选择的物联网轻量级入侵检测方法相关性系数和方差膨胀因子的特征选择方法。该方法在流粒度下对流量特征进行选择,通过机器学习算法对正常流量和恶意流量进行分类。实验结果表明,该方法能在有限的资源下快速有效地识别网络攻击行为,综合精确度与召回率达到99.4%。

With the large-scale use of the Internet of Things(IoT),the security problem has become increasingly prominent.How to detect network attacks accurately and in real time in the IoT environment with limited resources is a key problem that needs to be solved urgently.Intrusion detection system based on network traffic features is a solution to the security of IoT.This solution remains the problem of the large number of features make training fast and lightweight detection models difficult.To address this issue,this paper proposed a feature selection technique based on Pearson correlation coefficient and variance expansion factor.In this method,traffic characteristics were selected under flow granularity,and normal and malicious traffic were classified by machine learning algorithm.The experimental results show that this method can quickly and effectively detect network attacks with limited resources,and the overall precision and recall reach 99.4%.