DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE SUPPORT VECTOR MACHINE

AGUSTINI, KRISNA and Stiawan, Deris and Afifah, Nurul (2024) DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE SUPPORT VECTOR MACHINE. Undergraduate thesis, Sriwijaya University.

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Abstract

A botnet is a collection of devices infected with malware and controlled externally by an attacker to carry out network attacks, such as DDoS attacks, data theft, or spreading spam. This research uses a dataset from CICIoT2023 which consists of three types of classes, namely, tame traffic, mirai greip flood, and mirai udpplain to detect botnet malware attacks using the Support Vector Machine method. The Support Vector Machine method uses three types of kernels, namely, Linear, Polynomial and RBF kernels. The results of this research prove that the Support Vector Machine method using the RBF kernel is capable of detecting botnet malware attacks by achieving the best performance with an accuracy rate of 98.25%, precision of 98.58%, recall of 96.52%, and f1-score of 97.54%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Botnet Detection, CICIoT2023, RBF, Support Vector Machine
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:	Krisna Agustini
Date Deposited:	10 Jul 2024 06:58
Last Modified:	10 Jul 2024 06:58
URI:	http://repository.unsri.ac.id/id/eprint/150164

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