Detection of Most Recent Torjan on Operating System
Authors
Yogendra Tiwari
Department of Computer Science and Engineering, R D Engineering College, India (IN)
Rajeev Kaushi
Department of Computer Science and Engineering, R D Engineering College, India (IN)
Article Information
DOI: 10.51583/IJLTEMAS.2025.1407000038
Subject Category: computer science AI&ML
Volume/Issue: 14/7 | Page No: 335-338
Publication Timeline
Submitted: 2025-08-04
Published: 2025-08-04
Abstract
Abstract - The rapid growth of mobile technologies has expanded the functionality of smartphones, with Android emerging as the dominant platform due to its open ecosystem and massive user base exceeding 2 billion. However, this popularity has also made Android a primary target for cybercriminals, particularly in the distribution of mobile malware. Since 2009, mobile malware has escalated significantly, with a notable rise in new variants in 2017, fueled by profit-driven attackers and underground markets. Advanced evasion techniques such as obfuscation, logic bombs, and runtime payload dropping have made malware detection increasingly difficult. This thesis focuses on analyzing modern Android trojans—specifically, banking trojans that steal user credentials via fake login interfaces. Using both static reverse engineering and dynamic behavioral analysis in emulated environments, the study examines 2,380 samples across 20 trojan (sub)families. The research identifies common traits and evolutionary patterns among these threats, providing valuable insights into their increasing sophistication and the growing challenge they pose to existing mobile security solutions.
Keywords
Trojans, Android, antimalware
Downloads
References
1. apkdetect, https://app.apkdetect.com/, @pr3wtd, accessed: February 2020. [Google Scholar] [Crossref]
2. “Mobile operating system market share worldwide, 2020,” https://gs.statcounter.com/ os-market-share/mobile/worldwide, 2020. [Google Scholar] [Crossref]
3. E. Alepis and C. Patsakis, “Hey doc, is this normal?: exploring android permissions in the post marshmallow era,” in International Conference on Security, Privacy, and Applied Cryptography Engineering. Springer, 2017, pp. 53–73. [Google Scholar] [Crossref]
4. J. Gamba, M. Rashed, A. Razaghpanah, J. Tapiador, and N. Vallina-Rodriguez, “An analysis of pre-installed android software,” arXiv preprint arXiv:1905.02713, 2019. [Google Scholar] [Crossref]
5. L. Wei, Y. Liu, and S.-C. Cheung, “Taming android fragmentation: Characterizing and detecting compatibility issues for android apps,” in Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering, 2016, pp. 226–237. [Google Scholar] [Crossref]
6. M. Zheng, P. P. Lee, and J. C. Lui, “Adam: an automatic and extensible platform to stress test android anti-virus systems,” in International conference on detection of intrusions and malware, and vulnerability assessment. Springer, 2012, pp. 82–101. [Google Scholar] [Crossref]
7. V. Chebyshev, “Mobile malware evolution 2019,” https://securelist.com/ mobile-malware-evolution-2019/96280/, 2020. [Google Scholar] [Crossref]
8. C. Bai, Q. Han, G. Mezzour, F. Pierazzi, and V. Subrahmanian, “Dbank: Predictive behavioral analysis of recent android banking trojans,” IEEE Transactions on Dependable and Secure Computing, 2019. [Google Scholar] [Crossref]
9. Y. Zhang, G. Xiao, Z. Zheng, T. Zhu, I. Tsang, and Y. Sui, “An empirical study of code deobfuscations on detecting obfuscated android piggybacked apps,” in Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020, pp. 914–926. [Google Scholar] [Crossref]
10. Y. Tang, H. Wang, X. Zhan, X. Luo, Y. Zhou, H. Zhou, Q. Yan, Y. Sui, and J. W. Keung, “A systematical study on application performance management libraries for apps,” IEEE Transactions on Software Engineering, 2021. [Google Scholar] [Crossref]
11. L. Wang, R. He, H. Wang, P. Xia, Y. Li, L. Wu, Y. Zhou, X. Luo, Y. Sui, Y. Guo et al., “Beyond the virus: a first look at coronavirus-themed android malware,” Empirical Software Engineering, vol. 26, no. 4, pp. 1–38, 2021. [Google Scholar] [Crossref]
12. Saurabh Chauhan, Dharamveer Singh, Atul Kumar Singh (2022) “Artificial Intelligence In The Military: An Overview Of The Capabilities, Applications, And Challenges”, Journal of Survey in Fisheries Sciences, Vol 9 (2) pp 984-991. https://doi.org/10.53555/sfs.v9i2.2911 [Google Scholar] [Crossref]
13. Kiran, Dharamveer Singh, Nitin Goyal, (2023) “Analysis Of How Digital Marketing Affect By Voice Search”, Journal of Survey in Fisheries Sciences, Vol. 30 (2) 407-412. https://doi.org/10.53555/sfs.v10i3.2890 [Google Scholar] [Crossref]
14. Yukti Tyagi, Dharamveer Singh, Ramander Singh, Sudhir Dawra (2024) “Analysis Of The Most Recent Trojans On The Android Operating System”, Educational Administration: Theory and Practice, Vol. 30(2) 1320-1327. https://doi.org/10.53555/kuey.v30i2.6846 [Google Scholar] [Crossref]
15. Shivanee Singh, Dharamveer Singh, Ravindra Chauhan (2023) “Manufacturing Industry: A Sustainability Perspective On Cloud And Edge Computing”, Journal of Survey in Fisheries Sciences, pp 1592-1598. https://doi.org/10.53555/sfs.v10i2.2889 [Google Scholar] [Crossref]
Metrics
Views & Downloads
Similar Articles
- Advanced Digital Communication Strategies: A Comprehensive Analysis
- Developing an Explainable AI System for Digital Forensics: Enhancing Trust and Transparency in Flagging Events for Legal Evidence
- The Global Impact of Government Censorship on Women’s Access to Information: A Literature Review
- "Reimagining Higher Education Workspaces: A Review on The Transformative Role of Digital Technology Adoption"
- Improved CSP Efficiency: Innovations and Challenges in Thermal Energy Storage Systems