IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning

Published on Oct 1, 2020
· DOI :10.23919/EECSI50503.2020.9251304
Susanto (Sriwijaya University), M. Agus Syamsul Arifin (Sriwijaya University)+ 2 AuthorsRahmat Budiarto13
Estimated H-index: 13
(Al Baha University)
Botnet is one of the threats to internet network security-Botmaster in carrying out attacks on the network by relying on communication on network traffic. Internet of Things (IoT) network infrastructure consists of devices that are inexpensive, low-power, always-on, always connected to the network, and are inconspicuous and have ubiquity and inconspicuousness characteristics so that these characteristics make IoT devices an attractive target for botnet malware attacks. In identifying whether packet traffic is a malware attack or not, one can use machine learning classification methods. By using Weka and Scikit-learn analysis tools machine learning, this paper implements four machine learning algorithms, i.e.: AdaBoost, Decision Tree, Random Forest, and Naive Bayes. Then experiments are conducted to measure the performance of the four algorithms in terms of accuracy, execution time, and false positive rate (FPR). Experiment results show that the Weka tool provides more accurate and efficient classification methods. However, in false positive rate, the use of Scikit-learn provides better results.
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The Internet of Things (IoT) has rapidly transitioned from a novelty to a common, and often critical, part of residential, business, and industrial environments. Security vulnerabilities and exploits in the IoT realm have been well documented. In many cases, improving the security of an IoT device by hardening its software is not a realistic option, especially in the cost-sensitive consumer market or in legacy-bound industrial settings. As part of a multifaceted defense against botnet activity o...
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