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

FIRMANSYAH, MUHAMAD BAGAS and Stiawan, Deris and Hermansyah, Adi (2025) DETEKSI SERANGAN MALWARE APK REVERSE TCP MENGGUNAKAN METODE ANN (ARTIFICIAL NEURAL NETWORK) PADA SMALL BOARD COMPUTER. Undergraduate thesis, Sriwijaya University.

Preview	Image RAMA_56201_09011282126035_Cover.jpg - Cover Image Available under License Creative Commons Public Domain Dedication. Download (10MB) | Preview
	Text RAMA_56201_09011282126035.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_56201_09011282126035_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (709kB)
	Text RAMA_56201_09011282126035_0003047905_0030048909_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (700kB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (804kB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (135kB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (191kB) | Request a copy
	Text RAMA_56201_09011282126035_0003047905_0030048909_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (541kB) | Request a copy

Abstract

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.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Reverse TCP, Malware Android, Artificial Neural Network (ANN), Small Board Computer, Deteksi Intrusi
Subjects:	T Technology > T Technology (General) > T10.5-11.9 Communication of technical information > T10.68 Risk communication
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhamad Bagas Firmansyah
Date Deposited:	04 Sep 2025 03:36
Last Modified:	04 Sep 2025 03:36
URI:	http://repository.unsri.ac.id/id/eprint/183571

Actions (login required)

View Item