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1.

A machine learning technique for Android malicious attacks detection based on API calls Pages 29-44 Right click to download the paper Download PDF

Authors: Mousa AL-Akhras, Saud Alghamdi, Hani Omar, Hazzaa Alshareef

DOI: 10.5267/j.dsl.2023.12.004

Keywords: Attack Detection, API Calls, Machine Learning, Malware, Android

Abstract:
Android malware is widespread and it is considered as one of the most threatening attacks recently. The threat is targeting to damage access data or information or leaking them; in general, malicious software consists of viruses, worms, and other malware. Current malware attempts to prevent being detected by any software or anti-virus. This paper describes recent Android malware detection static and interactive approaches as well as several open-source malware datasets. The paper also examines the most current state-of-the-art Android malware identification techniques including identifying by comparative evaluation the gaps between these techniques. As a result, an API-based dynamic malware detection framework is proposed for Android to provide a dynamic paradigm for malware detection. The proposed framework was closely inspected and checked for reliability where meaningful API packages and methods were discovered.
Details
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Journal: DSL | Year: 2024 | Volume: 13 | Issue: 1 | Views: 1107 | Reviews: 0

 

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