Intrusion Detection System Based on Evolving Rules for Wireless Sensor Networks

Abstract

Human care services, as one of the classical Internet of things applications, enable various kinds of things to connect with each other through wireless sensor networks (WSNs). Owing to the lack of physical defense devices, data exchanged through WSNs such as personal information is exposed to malicious attacks. Therefore, intrusion detection is urgently needed to actively defend against such attacks. Intrusion detection as a data mining procedure cannot control the size of rule sets and distinguish the similarity between normal and intrusion network behaviors. Therefore, in this paper, an evolving mechanism is introduced to extract the rules for intrusion detection. To extract diversified rules as well as control the quantity of rulesets, the extracted rules are examined according to the distance between the rules in the rule set of the same class and the rules in the rule set of different classes. Thereby, it alleviates the problem that the quantity of rules expands unexpectedly with the evolving genetic network programming. The simulations are conducted on a benchmark intrusion dataset, and the results show that the proposed method provides an effective solution to evolve the class association rules and improves the intrusion detection performance.