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.
Highlights
Wireless sensor networks are composed of several sensors deployed in areas where the aim is to collect data and forward it for the analysis
The main scope of this paper is to improve that random forest technique is an efficient anomaly detection technique for Intrusion detection system (IDS) in Wireless sensor network (WSN), with a comparative evaluation study for the most recent and performants anomaly detection technique used in IDS for WSN
Step1: in this step we structured all records on AttributeRelation File Format (ARFF), which is an input file format used by the machine learning tool WEKA [33]
Summary
Equipe Signaux, Systèmes et Informatique (ESSI) National School of Applied Sciences, Ibn Zohr University. Laboratoire Thermodynamique et Energétique Faculty of Sciences, Ibn Zohr University AGADIR, MOROCCO. 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. To implement and measure the performance of detection techniques we prepare our dataset, based on KDD'99, into five steps, after normalizing our dataset, we determined normal class and 4 types of attacks, and used the most relevant attributes for the classification process.
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