文章基本信息

标题：Performance Evaluation of Machine Learning Techniques for DOS Detection in Wireless Sensor Network
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作者：Lama Alsulaiman ; Saad Al-Ahmadi
期刊名称：International Journal of Network Security & Its Applications
印刷版ISSN：0975-2307
电子版ISSN：0974-9330
出版年度：2021
卷号：13
期号：2
页码：21-29
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The nature of Wireless Sensor Networks (WSN) and the widespread of using WSN introduce many security threats and attacks. An effective Intrusion Detection System (IDS) should be used to detect attacks. Detecting such an attack is challenging, especially the detection of Denial of Service (DoS) attacks. Machine learning classification techniques have been used as an approach for DoS detection. This paper conducted an experiment using Waikato Environment for Knowledge Analysis (WEKA)to evaluate the efficiency of five machine learning algorithms for detecting flooding, grayhole, blackhole, and scheduling at DoS attacks in WSNs. The evaluation is based on a dataset, called WSN-DS. The results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.
关键词：Wireless Sensor Networks;Machine Learning;Denial of Service