PERBANDINGAN METODE SELEKSI FITUR PADA SISTEM KLASIFIKASI BOTNET IoT MENGGUNKAN ALGORITMA RANDOM FOREST

QURAHMAN, M. TAUFIQ and Stiawan, Deris (2021) PERBANDINGAN METODE SELEKSI FITUR PADA SISTEM KLASIFIKASI BOTNET IoT MENGGUNKAN ALGORITMA RANDOM FOREST. Undergraduate thesis, Sriwijaya University.

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Abstract

Botnet attacks are one of the most serious threats of many threats in the rapid development of Internet of Things (IoT) devices. The more complex IoT devices make the detection or classifying time of attacks longer and consume a lot of memory. This study used MedBIoT datasets from Tallinn University Of Technology. Extra trees feature selection method and correlation feature selection are applied to select the best features. In addition, the random forest algorithm is also applied to the classification process. Classification results using selected features are able to obtain excellent levels of accuracy, sensitivity, specificity, precision, and F1 scores with faster processing times and with relatively low levels of misclassification.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Internet of Things, Botnet Classification, Feature Selection, Random Forest, Machine Learning,
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Taufiq Qurahman
Date Deposited:	04 Aug 2021 06:31
Last Modified:	04 Aug 2021 06:31
URI:	http://repository.unsri.ac.id/id/eprint/51127

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