Skripsi

DETEKSI SERANGAN MALWARE APK REVERSE TCP MENGGUNAKAN METODE ANN (ARTIFICIAL NEURAL NETWORK) PADA SMALL BOARD COMPUTER

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Penilaian

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The increasing use of Android devices has also led to the rise of security threats, one of which is the Reverse TCP attack technique that enables unauthorized remote access to victim devices. This study aims to develop a malware detection system using the Artificial Neural Network (ANN) method, implemented on a Small Board Computer (SBC), specifically the Banana Pi BPI-R1. Data was collected through simulated reverse TCP attacks using Metasploit, analyzed with CICFlowMeter, and processed for ANN model training. The testing results indicated that the ANN model successfully detected attack activity with an accuracy of 98.95%, precision of 98.92%, recall of 99.33%, and an F1-score of 99.12%. The system effectively distinguished between normal and malicious network traffic and demonstrated superior detection performance compared to rule-based IDS tools such as Snort and Suricata. Its implementation on a resource-constrained device confirms that the ANN method is a reliable and efficient solution for malware detection. Keywords: Reverse TCP, Android Malware, Artificial Neural Network (ANN), Small Board Computer, Intrusion Detection System.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2507005586	T183571	T1835712025	Central Library (Reference)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1835712025

Publisher

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

Collation

xiii, 65 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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