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Title |
Mitigating Mobile Malware Threats: Implementing Gaussian Na?ve Bayes for Effective Banking Trojan Detection |
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Author |
Najiahtul Syafiqah Ismail, Anis Athirah Masmuhallim, Mohd Talmizie Amron, Fazlin Marini Hussain, Nadiathul Raihana Ismail |
| Citation |
Vol. 24 No. 10 pp. 17-24 |
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Abstract |
Mobile phones have become immensely popular as intelligent terminals worldwide. The open-source nature of mobile platforms has facilitated the development of third-party mobile applications, but it has also created an environment for mobile malware to thrive. Unfortunately, the abundance of mobile applications and lax management of some app stores has led to potential risks for mobile users, including privacy breaches and malicious deductions of fees, among other adverse consequences. This research presents a mobile malware static detection method based on Gaussian Na?ve Bayes. The approach aims to offer a solution to protect users from potential threats such as Banking Trojan malware. The objectives of this project are to study the requirement of the Na?ve Bayes algorithm in Mobile Banking Trojan detection, and to evaluate the performance and accuracy of the Gaussian Na?ve Bayes algorithm in the Mobile Banking Trojan detection. This study presents a mobile banking trojan detection system utilizing the Gaussian Na?ve Bayes algorithm, achieving a high classification accuracy of 95.83% in distinguishing between benign and trojan APK files. |
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Keywords |
Banking Trojan, Gaussian Na?ve Bayes, Mobile Malware, Mobile Security, Static Detection. |
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