DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE LOGISTIC REGRESSION

ANGGRAENI, CYNTHIA and Stiawan, Deris and Afifah, Nurul (2025) DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE LOGISTIC REGRESSION. Undergraduate thesis, Sriwijaya University.

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Abstract

Botnets are a serious cyberattack threat that infects computer networks controlled by botmasters to carry out malicious activities. Various types of botnets have emerged over the years, posing a significant threat to cybersecurity. These botnets' malicious activities vary from executing instruction-based attacks such as DDoS attacks, flooding, and spamming. This study used the CICIoT2023 dataset, which consists of three classes: benign traffic, Mirai Greip Flood, and Mirai Upplain, to detect botnet malware attacks using the Logistic Regression method. The results showed that the Multinomial Logistic Regression model achieved an accuracy of 88.19%, a precision of 92.73%, a recall of 87.93%, and an F1-Score of 90.26%. Keywords: Botnet Detection, CICIoT2023, Logistic Regression.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Botnet Detection, CICIoT2023, Logistic Regression.
Subjects:	T Technology > T Technology (General) > T57.6-57.97 Operations research. Systems analysis > T57.85 Network systems theory Including network analysis Cf. TS157.5+ Scheduling
Divisions:	09-Faculty of Computer Science > 55201-Informatics (S1)
Depositing User:	cynthia anggraeni
Date Deposited:	16 Sep 2025 07:51
Last Modified:	16 Sep 2025 07:51
URI:	http://repository.unsri.ac.id/id/eprint/184037

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