DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE K-NEAREST NEIGHBOR

KHAERONISYAH, SITI and Stiawan, Deris and Afifah, Nurul (2024) DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE K-NEAREST NEIGHBOR. Undergraduate thesis, Sriwijaya University.

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Abstract

A Mirai Botnet is a computer network consisting of thousands or millions of internet connected devices that have been hacked by the mirai malware. The purpose of the Mirai Botnet is to control Internet of Things (IoT) devices with weak security to infect the device and turn it into a botnet that can target other devices through Distributed Denial of Service (DDoS) attacks that can paralyze web services. This study uses a dataset from CICIoT2023 which consists of three types of classes namely benign traffic, mirai greip flood, and mirai udpplain to detect botnet malware attacks using the K-Nearest Neighbor method. The results of this study show that the K-Nearest Neighbor method using the k=12 value is able to detect botnet malware attacks by achieving the best performance with an accuracy rate of 98.50%, precision of 98.19%, recall of 96.46%, and f1-score of 97.32%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Botnet Mirai, Botnet Detection, CICIoT2023, K-Nearest Neighbor
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:	Siti Khaeronisyah
Date Deposited:	10 Jul 2024 07:00
Last Modified:	10 Jul 2024 07:00
URI:	http://repository.unsri.ac.id/id/eprint/150165

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