Intrusion Detection Techniques in Wireless Sensor Network using Data Mining Algorithms: Comparative Evaluation Based on Attacks Detection

TLDR

Wireless sensor networks, composed of resource‑constrained nodes deployed in open environments, are vulnerable to a range of attacks, making intrusion detection systems essential for maintaining security. This study conducts a comparative evaluation of the most effective intrusion detection techniques for WSNs, including an attribute‑selection strategy to improve classification performance. Using a KDD'99‑derived dataset, the authors classify attacks, normalize data, select relevant attributes with CfsSubsetEval and BestFirst, and apply various detection algorithms to assess performance. Random forest classifiers achieve the highest detection rates while minimizing false alarms, and the authors propose guiding principles and recommendations for future IDS research in WSNs.

Abstract

Wireless sensor network (WSN) consists of sensor nodes. Deployed in the open area, and characterized by constrained resources, WSN suffers from several attacks, intrusion and security vulnerabilities. Intrusion detection system (IDS) is one of the essential security mechanism against attacks in WSN. In this paper we present a comparative evaluation of the most performant detection techniques in IDS for WSNs, the analyzes and comparisons of the approaches are represented technically, followed by a brief. Attacks in WSN also are presented and classified into several criteria. To implement and measure the performance of detection techniques we prepare our dataset, based on KDD'99, into five step, after normalizing our dataset, we determined normal class and 4 types of attacks, and used the most relevant attributes for the classification process. We propose applying CfsSubsetEval with BestFirst approach as an attribute selection algorithm for removing the redundant attributes. The experimental results show that the random forest methods provide high detection rate and reduce false alarm rate. Finally, a set of principles is concluded, which have to be satisfied in future research for implementing IDS in WSNs. To help researchers in the selection of IDS for WSNs, several recommendations are provided with future directions for this research.