Open AccessModified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection
Author(s) -
Subarna Shakya
Publication year - 2021
Publication title -
iro journal on sustainable wireless systems
Language(s) - English
Resource type - Journals
ISSN - 2582-3167
DOI - 10.36548/jsws.2021.2.006
Subject(s) - intrusion detection system , computer science , constant false alarm rate , artificial intelligence , feature selection , wireless sensor network , false alarm , machine learning , intrusion , gray wolf , selection (genetic algorithm) , false positive rate , wireless , data mining , pattern recognition (psychology) , computer network , telecommunications , paleontology , geochemistry , canis , biology , geology
The ability of wireless sensor networks (WSN) and their functions are degraded or eliminated by means of intrusion. To overcome this issue, this paper presents a combination of machine learning and modified grey wolf optimization (MLGWO) algorithm for developing an improved intrusion detection system (IDS). The best number of wolves are found by running tests with multiple wolves in the model. In the WSN environment, the false alarm rates are reduced along with the reduction in processing time while improving the rate of detection and the accuracy of intrusion detection with a decrease in the number of resultant features. In order to evaluate the performance of the proposed model and to compare it with the existing techniques, the NSL KDD’99 dataset is used. In terms of detection rate, false alarm rate, execution time, total features and accuracy the evaluation and comparison is performed. From the evaluation results, it is evident that higher the number of wolves, the performance of the MLGWO model is enhanced.