Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)
نویسندگان
چکیده
A wormhole attack is a type of on the network layer that reflects routing protocols. The classification performed with several methods machine learning consisting K -nearest neighbor (KNN), support vector (SVM), decision tree (DT), linear discrimination analysis (LDA), naive Bayes (NB), and convolutional neural (CNN). Moreover, we used nodes’ properties for feature extraction, especially speed, in MANET. We have collected 3997 distinct (normal 3781 malicious 216) samples comprise normal nodes. results show accuracy KNN, SVM, DT, LDA, NB, CNN are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, 96.4%, respectively. Based our findings, DT method’s 98.9% higher than other ways. In next priority, CNN, NB indicate high accuracy,
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ژورنال
عنوان ژورنال: Wireless Communications and Mobile Computing
سال: 2022
ISSN: ['1530-8669', '1530-8677']
DOI: https://doi.org/10.1155/2022/2375702