Abstract
Wireless Sensor Networks is extensively used in many of applications related to different fields. Some of those applications deal with confidential and critical data that must be protected from unauthorized access. Some other systems use WSNs that are deployed in very harsh environments with limited energy resources. Those systems cannot tolerate network failures that can be caused by network intruders. In this paper, an efficient intrusion detection model is introduced. The model uses intelligent techniques to detect intrusions. Two different architectures are introduced. The first architecture represents the level of sensor node, sink node, and base station. The second architecture represents the levels of sensor and sink nodes. This work proposes two intrusion detection algorithms, one uses a supervised learning mechanism to be used on the level of the sensor node and the other uses an unsupervised learning mechanism to be used on the levels of both the sink node and base station. The output of the algorithms is a set of detection rules which are structured in the form of binary tree. The introduced algorithms provided a high detection accuracy using less number of selected features, compared to previous work for intrusion detection, which decreases the complexity and the processing time. The proposed learning algorithms used only 10% of the data for training. An enhancement for J48 classification algorithm is also introduced which decreases the size of the algorithm's decision tree and makes it suitable to be used for intrusion detection in WSNs.