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Feature Point Detection for Repacked Android Apps

M. A. Rahim Khan*, Manoj Kumar Jain

Department of Computer Science and Engineering, Lingaya’s Vidyapeeth, Faridabad, 121002, India

* Corresponding Author: M. A. Rahim Khan. Email:

Intelligent Automation & Soft Computing 2020, 26(6), 1359-1373.


Repacked mobile applications and obfuscation attacks constitute a significant threat to the Android technological ecosystem. A novel method using the Constant Key Point Selection and Limited Binary Pattern Feature (CKPS: LBP) extraction-based Hashing has been proposed to identify repacked Android applications in previous works. Although the approach was efficient in detecting the repacked Android apps, it was not suitable for detecting obfuscation attacks. Additionally, the time complexity needed improvement. This paper presents an optimization technique using Scalable Bivariant Feature Transformation extract optimum feature-points extraction, and the Harris method applied for optimized image hashing. The experiments produced better results than the CKPS: LBP method in terms of execution time. Further, the proposed method is extended to detect obfuscation of malware attacks by detecting the packed executables, which is the initial step in obfuscation attack detection.


Cite This Article

M. A. Rahim Khan and M. Kumar Jain, "Feature point detection for repacked android apps," Intelligent Automation & Soft Computing, vol. 26, no.6, pp. 1359–1373, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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