Artificial Intelligence based Algorithm for Detecting Android Obfuscated Applications | ||||
| International Journal of Intelligent Computing and Information Sciences | ||||
| Volume 24, Issue 1, March 2024, Page 1-11 PDF (571.22 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/ijicis.2024.250295.1308 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
Hend Faisal Aboud  1; Hanan Hindy2; Samir Gaber3; Abdel-Badeeh M. Salem 4
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| 1Computer science, Ainshams University, Egypt | ||||
| 2Faculty of Computer and Information Sciences, Ain Shams University, Egypt | ||||
| 3Faculty of Engineering in Helwan, Helwan University | ||||
| 4Computer Sciece Department, Faculty of Computer and Information Sciences, Ain Shams University | ||||
| Abstract | ||||
| As technology continues to advance, so does the landscape of Android; based on its open-source nature which renders it vulnerable to various risks. Therefore, the developers need to deploy and employ obfuscation techniques in their newly developed android applications. In this paper , we present an investigation into Android obfuscation detection. Our work encompasses a comprehensive examination of Android obfuscation techniques and an exploration of their intersection with machine learning. We conducted extensive experiments involving various machine learning models to detect obfuscation. Among these models , The results show that Random Forest is the one with the most promising results with accuracy 99.5% in detecting Android Obfuscation. The dataset utilized in the experiments encompasses a diverse range of samples, including both malicious and benign samples. This diversity allows for a robust evaluation of the effectiveness of obfuscation detection across different scenarios and highlights the challenges posed by varying obfuscation techniques. | ||||
| Keywords | ||||
| Android Obfuscation; Machine Learning; Artificial Intelligence; Information Security | ||||
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