基于机器学习的物联网异常流量检测方法

Abnormal traffic detection method of Internet of things based on machine learning

随着物联网的广泛应用,物联网的安全问题受到越来越多的关注。针对物联网环境下异常网络流量问题,提出了基于机器学习的物联网异常流量检测方法。首先通过使用聚类算法分析物联网一段时间内网络数据的特征,然后使用连续假设检验算法对特征进行分类,并对恶意流量的空间分布进行二次特征分析。实验表明,相对于传统的异常流量检测方法,该检测方法具有更高的检测效率和精度。

With the wide application of the Internet of things,the security of the Internet of things has attracted more and more attention.Aiming at the problem of abnormal network traffic in the Internet of things environment,an abnormal traffic detection method based on machine learning is proposed.Firstly,the clustering algorithm is used to analyze the characteristics of network data in the Internet of things for a period of time,then the continuous hypothesis test algorithm is used to classify the characteristics,and the spatial distribution of malicious traffic is analyzed.Experiments show that compared with the traditional abnormal flow detection method,this detection method has higher detection efficiency and accuracy.