Skripsi

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%.

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Availability

Detail Information

Series Title

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Call Number

T1906802026

Publisher

Indralaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2026

Collation

xiii, 85 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.807

Content Type

Text

Media Type

unmediated

Carrier Type

-

Edition

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Subject(s)

Prodi Sistem Komputer
Logistic Regression

Specific Detail Info

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Statement of Responsibility

KA

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