Visible to the public IoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning

TitleIoT Botnet Malware Classification Using Weka Tool and Scikit-learn Machine Learning
Publication TypeConference Paper
Year of Publication2020
AuthorsSusanto, Stiawan, D., Arifin, M. A. S., Idris, M. Y., Budiarto, R.
Conference Name2020 7th International Conference on Electrical Engineering, Computer Sciences and Informatics (EECSI)
Date PublishedOct. 2020
ISBN Number 978-602-0737-62-1
Keywordsaccurate classification methods, botmaster, Botnet, botnet IoT, botnet malware attacks, botnets, classification, composability, computer network security, Decision trees, false positive rate, inconspicuousness characteristics, inexpensive power, internet network security, Internet of Things, Internet of Things network infrastructure, invasive software, IoT botnet malware classification, IoT devices, learning (artificial intelligence), machine learning, machine learning algorithms, Malware, malware attack, Metrics, network traffic, packet traffic, pubcrawl, resilience, Resiliency, scikit-learn, Scikit-learn analysis tools machine learning, Scikit-learn machine learning, telecommunication traffic, Time measurement, Tools, WEKA, weka tool

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.

Citation Keysusanto_iot_2020