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DETEKSI SERANGAN MALWARE BOTNET MENGGUNAKAN METODE LOGISTIC REGRESSION

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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

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Series Title

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Call Number

T1840372025

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2025

Collation

xiii, 88 hlm.; ilus.; tab.; 29 cm.

Language

Indonesia

ISBN/ISSN

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Classification

005.13

Content Type

Text

Media Type

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Carrier Type

-

Edition

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Subject(s)

Sistem Pemograman
Prodi Sistem Komputer, Fakultas Ilmu Komputer

Specific Detail Info

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Statement of Responsibility

NO

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