Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network

• URL: http://arxiv.org/abs/2104.01963v1
• Date: Mon, 5 Apr 2021 15:31:27 GMT
• Title: Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network
• Authors: Lama Alsulaiman and Saad Al-Ahmadi
• Abstract summary: 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 results showed that the random forest classifier outperforms the other classifiers with an accuracy of 99.72%.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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%.

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