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Open AccessArticle

A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks

Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
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Sensors 2019, 19(20), 4383; https://doi.org/10.3390/s19204383
Received: 2 September 2019 / Revised: 24 September 2019 / Accepted: 30 September 2019 / Published: 10 October 2019
An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable results. However, these works still need to be more accurate and efficient against imbalanced data problems in network traffic. In this paper, we proposed a new model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoot) classifier, called GXGBoost model. The latter is a gradient boosting model designed for improving the performance of traditional models to detect minority classes of attacks in the highly imbalanced data traffic of wireless sensor networks. A set of experiments were conducted on wireless sensor network-detection system (WSN-DS) dataset using holdout and 10 fold cross validation techniques. The results of 10 fold cross validation tests revealed that the proposed approach outperformed the state-of-the-art approaches and other ensemble learning classifiers with high detection rates of 98.2%, 92.9%, 98.9%, and 99.5% for flooding, scheduling, grayhole, and blackhole attacks, respectively, in addition to 99.9% for normal traffic. View Full-Text
Keywords: intrusion detection system; wireless sensor networks; genetic algorithm; extreme gradient boosting classifier; WSN-DS intrusion detection system; wireless sensor networks; genetic algorithm; extreme gradient boosting classifier; WSN-DS
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Alqahtani, M.; Gumaei, A.; Mathkour, H.; Maher Ben Ismail, M. A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks. Sensors 2019, 19, 4383.

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