- 907 PC 33 SOFTWARE DEVELOPER CRACKED
- 907 PC 33 SOFTWARE DEVELOPER ANDROID
- 907 PC 33 SOFTWARE DEVELOPER SOFTWARE
- 907 PC 33 SOFTWARE DEVELOPER CODE
907 PC 33 SOFTWARE DEVELOPER ANDROID
However, malware authors use the above-mentioned advanced Java features and evasion tools to reproduce more sophisticated Android malware, evading professional anti-malware ( Preda & Maggi, 2016).
907 PC 33 SOFTWARE DEVELOPER SOFTWARE
Likewise, Java uses obfuscation tools ( Aonzo et al., 2020 GuardSquare, 2014) to protect commercial software companies from software plagiarism issues professional developers protect their source codes from being stolen using advanced evasion techniques ( Aonzo et al., 2020) as protection mechanisms.
907 PC 33 SOFTWARE DEVELOPER CODE
Android applications use Java as a developing language because Java provides a very flexible code, dynamic code loading ( Liang & Bracha, 1998), and many other features to make Android application development more accessible and efficient. They mainly aim to spy on private data ( e.g., contact lists, photos, videos, documents, and account details) or control devices by remote servers as botnets ( Karim et al., 2015).
907 PC 33 SOFTWARE DEVELOPER CRACKED
Nevertheless, Android malware authors attract end-users using cracked games, free applications, and video downloader applications. In the Google Android market, Android applications exponentially grow from 2.8 million as of September 2018 ( Statista, 2016, 2021), to almost double, to reach 3.4 million apps as of the first quarter of 2021 ( Statista, 2021). Therefore, the number of Android malware increases exponentially. With many open-source libraries in Android, Android application development tools enable young developers to develop Android malware applications. Hence, Android devices have become the most popular devices for hackers and malware authors to target. Since the advent of Android systems, smartphone devices are seen everywhere with a market share of 87% ( Chau & Reith, 2019). The study concludes the recent Android malware detection-related issues and lessons learned which require researchers’ attention in the future. The study concludes the research gaps in evaluating the current Android malware detection framework robustness against state-of-the-art evasion techniques.
The study criticizes the existing research gap of detection in the latest Android malware detection frameworks and challenges the classification performance against various evasion techniques. This study reviews the state-of-the-art evasion tools and techniques. The concern of encountering difficulties in malware reverse engineering motivates researchers to secure the source code of benign Android applications using evasion techniques. Malware authors use obfuscation techniques to generate new malware variants from the same malicious code. The malware authors adopt the obfuscation and transformation techniques to defeat the anti-malware detections, which this paper refers to as evasions. Therefore, Android applications developers tend to use state-of-the-art obfuscation techniques to mitigate the risk of application plagiarism. Android malware hackers adopt reverse engineering and repackage benign applications with their malicious code.
Every day, thousands of new Android malware applications emerge. The various application markets are facing an exponential growth of Android malware.
The rise of obfuscated Android malware and impacts on detection methods. Cite this article Elsersy WF, Feizollah A, Anuar NB. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. Licence This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. Department of Computer System and Technology/Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan Kuala Lumpur, Malaysia DOI 10.7717/peerj-cs.907 Published Accepted Received Academic Editor Muhammad Aleem Subject Areas Data Mining and Machine Learning, Mobile and Ubiquitous Computing, Security and Privacy, Operating Systems Keywords Android malware, Android security, Evasion techniques, Machine learning, Obfuscation techniques Copyright © 2022 Elsersy et al.
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