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Identification of botnet attacks using hybrid machine learning models
Published in Springer
Volume: 1179 AISC
Pages: 249 - 257
Botnet attacks are the new threat in the world of cyber security. In the last few years with the rapid growth of IoT based Technology and networking systems connecting large number of devices, attackers can deploy bots on the network and perform large scale cyber-attacks which can affect anything from millions of personal computers to large scale organizations. Hence, there is a necessity to implement countermeasures to over-come botnet attacks. In this paper, three hybrid models are proposed which are developed by integrating multiple machine learning algorithms like Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), K-Nearest Neighbor (KNN) and Linear Regression (LR). According to our experimental analysis, the RF-SVM has the highest accuracy (85.34%) followed by RF-NB-K-NN (83.36%) and RF-KNN-LR (79.56%). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.
About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing
PublisherData powered by TypesetSpringer