Intrusion Detection Based Learning in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) have different limitations, including storage and processing capabilities. Besides, sensors' communication ranges are limited. Besides, those limitations raise the issue of the network intrusion and sensors' capabilities in detecting intruders. This paper introduces two different algorithms to detect intrusions in wireless sensor-based systems, which are more vulnerable to attacks. The first proposed algorithm is supervised learning-based classification. On the other hand, the second algorithm is unsupervised learning-based clustering. Both algorithms try to detect intrusions using a set of detection rules that are structured in the form of decision trees. The algorithms are detailed and extensively examined on a well-known dataset. They also are tested against two different architectures, two and three levels networks. The three-level architecture represents the sensor node, sink node, and base station level, while the two-level architecture represents the levels of sensor and sink nodes. An enhancement for decision-tree-based classification algorithms is also proposed by changing the decision tree to a binary tree. Such change made a significant enhancement in the complexity of reaching a decision. The produced decision trees use a similar decision tree node structure as the one used in Classification and Regression Trees (CART). The performance of our proposed algorithms and techniques are measured and extensively examined.