Performance Evaluation of Machine Learning Techniques for DoS Detection in Wireless Sensor Network
This work addresses security threats in Wireless Sensor Networks, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of detecting Denial of Service (DoS) attacks in Wireless Sensor Networks by evaluating five machine learning algorithms, finding that the random forest classifier achieved the highest accuracy of 99.72%.
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%.