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

Sensors (Basel, Switzerland), 2019-10, Vol.19 (20), p.4383 [Peer Reviewed Journal]

2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019 by the authors. 2019 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19204383 ;PMID: 31658774

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  • Title:
    A Genetic-Based Extreme Gradient Boosting Model for Detecting Intrusions in Wireless Sensor Networks
  • Author: Alqahtani, Mnahi ; Mathkour, Hassan ; Mohamed Maher Ben Ismail
  • Subjects: Algorithms ; Artificial intelligence ; Classifiers ; extreme gradient boosting classifier ; Flooding ; genetic algorithm ; Genetic algorithms ; Internet of Things ; intrusion detection system ; Intrusion detection systems ; Literature reviews ; Methods ; Optimization ; Sensors ; Support vector machines ; Taxonomy ; Threats ; Wireless networks ; Wireless sensor networks ; wsn-ds
  • Is Part Of: Sensors (Basel, Switzerland), 2019-10, Vol.19 (20), p.4383
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1424-8220
    EISSN: 1424-8220
    DOI: 10.3390/s19204383
    PMID: 31658774
  • Source: Geneva Foundation Free Medical Journals at publisher websites
    PubMed Central
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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