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• W4226127777 abstract "A wormhole attack is a type of attack on the network layer that reflects routing protocols. The classification is performed with several methods of machine learning consisting of <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>K</mi> </math> -nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), linear discrimination analysis (LDA), naive Bayes (NB), and convolutional neural network (CNN). Moreover, we used nodes’ properties for feature extraction, especially nodes’ speed, in the MANET. We have collected 3997 distinct (normal 3781 and malicious 216) samples that comprise normal and malicious nodes. The classification results show that the accuracy of the KNN, SVM, DT, LDA, NB, and CNN methods are 97.1%, 98.2%, 98.9%, 95.2%, 94.7%, and 96.4%, respectively. Based on our findings, the DT method’s accuracy is 98.9% and higher than other ways. In the next priority, SVM, KNN, CNN, LDA, and NB indicate high accuracy, respectively." @default.
• W4226127777 created "2022-05-05" @default.
• W4226127777 creator A5001755097 @default.
• W4226127777 creator A5039930701 @default.
• W4226127777 date "2022-01-31" @default.
• W4226127777 modified "2023-09-26" @default.
• W4226127777 title "Machine Learning Methods for Intrusive Detection of Wormhole Attack in Mobile Ad Hoc Network (MANET)" @default.
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• W4226127777 doi "https://doi.org/10.1155/2022/2375702" @default.
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