Skripsi

DETEKSI SERANGAN MALWARE ANDROID REVERSE TCP PADA SMALL BOARD COMPUTER DENGAN METODE NAÏVE BAYES

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Penilaian

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Android malware threats using reverse TCP techniques are increasing and require effective detection methods. This study develops a detection systemusing the Naïve Bayes algorithm implemented on a small board computer (Banana Pi). Network traffic datasets were created under three scenarios: normal, attack (reverse TCP), and combined. After labeling and preprocessing, the Naïve Bayes model was tested and achieved an accuracy of 97.56%, with 91.00% precision, 89.47% recall, and a 90.23% F1-score. The model’s performance was compared to two signature-based detection systems, Snort and Suricata, and showed more effective results, particularly in the combined scenario. These findings demonstrate that simple machine learning methods like Naïve Bayes can provide reliable malware detection, especially when applied on lightweight and low-cost devices such as SBCs.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005584	T183573	T1835732025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1835732025

Publisher

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

Collation

xvi, 85 hlm.; ilus,; tab, 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.840 7

Content Type

Text

Media Type

unmediated

Carrier Type

other (computer)

Edition

-

Subject(s)

Malware
Prodi Sistem Komputer

Specific Detail Info

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Statement of Responsibility

SEPTA

Other version/related

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