  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%.
