ARCHIVES
Original Article
Unmasking the Android Scam in Apps: Tactics, Ecosystem and Threats
Aswandh Sree Dev T.N.A1
Dr.F.Ramesh Dhanaseelan2
Dr. M. Jeya Sutha3
1Department of Computer Applications, St. Xavier’s Catholic College of Engineering, Chunkankadai, Nagercoil, Tamilnadu, India. 2Professor, Department of Computer Applications, St. Xavier’s Catholic College of Engineering, Chunkankadai, Nagercoil, Tamilnadu, India. 3Associate Professor, Department of Computer Applications, St. Xavier’s Catholic College of Engineering, Chunkankadai, Nagercoil, Tamilnadu, India.
Published Online: July-August 2025
Pages: 01-06
Cite this article
↗ https://www.doi.org/10.59256/ijire.20250604001
References
1.A. Arvindh, Towards Trustworthy Digital Ecosystem: From Fair Representation Learning to Fraud Detection, 2024.
2. A. Rawat, A. Kumar, A. Singh, and Dr. Arora, “Exploring Android Security Landscape: Threats, Vulnerabilities, and Best Practices,” International Research Journal on Advanced Engineering and Management (IRJAEM), vol. 2, pp. 1831–1839, 2024, doi:10.47392/IRJAEM.2024.0271.
3. S. Amir, D. F. Priambodo, A. A. Ajhari, and A. Widyasuri, “Analysis of Fraud Attacks Using Android Package Kit in Indonesia,” in 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia, 2024, pp. 285–290, doi: 10.1109/IC3INA64086.2024.10732435. [4] R. Kumar, Exploiting App Differences for Security Analysis of Multi- Geo Mobile Ecosystems, Ph.D. dissertation, 2023.
5.J. K. Nithyanandam et al., “Recognizing the misleading dating app ecosystem,” American Institute of Physics, 2023, doi:
10.1063/5.0164404.
6 Y. Han, S. Wang, Y. Li, X. Cao, L. Huang, and Z. Chen, “Measurement of Illegal Android Gambling App Ecosystem From Joint Promotion Perspective,” in 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece, 2023, pp. 1–11, doi: 10.1109/DSAA60987.2023.10302499.
7. S. Sebastian and J. Caballero, “Towards attribution in mobile markets: Identifying developer account polymorphism,” in Proc. 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020, pp. 771–785.
8. K. A. Roundy et al., “The many kinds of creepware used for interpersonal attacks,” in 2020 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2020, pp. 626–643, doi: 10.1109/SP40000.2020.00069.
9. S. Seraj, M. Pavlidis, M. Trovati, and N. Polatidis, “MadDroid: malicious adware detection in Android using deep learning,” Journal of Cyber Security Technology, vol. 8, no. 3, pp. 163–190, 2023, doi: 10.1080/23742917.2023.2247197.
10. E. Arul and A. Punidha, “Adware attack detection on IoT devices using deep Logistic Regression SVM (DL-SVM-IoT),” in Congress on Intelligent Systems: Proceedings of CIS 2020, vol. 1, pp. 167–176, Springer Singapore, 2021.
11. Z. Chen et al., “Deuedroid: Detecting underground economy apps based on UTG similarity,” in Proc. 32nd ACM SIGSOFT Int. Symp. Softw. Testing Anal., 2023, pp. 223–235.
12. Z. Gu, Z. Xu, H. Chen, J. Lan, C. Meng, and W. Wang, “Mobile user interface element detection via adaptively prompt tuning,” in Proc.IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 11155– 11164.
13. Y. Hu et al., “Dating with scambots: Understanding the ecosystem of fraudulent dating applications,” IEEE Trans. Dependable Secure Comput., vol. 18, no. 3, pp. 1033–1050, May/Jun. 2021.
14 P. Kotzias, J. Caballero, and L. Bilge, “How did that get in my phone? Unwanted app distribution on Android devices,” in Proc. IEEE Symp. Secur. Privacy, 2021, pp. 53–69.
15 K. A. Roundy et al., “The many kinds of creepware used for interpersonal attacks,” in Proc. IEEE Symp. Secur. Privacy, 2020, pp. 626–643.
16 S. Sebastian and J. Caballero, “Towards attribution in mobile markets: Identifying developer account polymorphism,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2020, pp. 771–785
2. A. Rawat, A. Kumar, A. Singh, and Dr. Arora, “Exploring Android Security Landscape: Threats, Vulnerabilities, and Best Practices,” International Research Journal on Advanced Engineering and Management (IRJAEM), vol. 2, pp. 1831–1839, 2024, doi:10.47392/IRJAEM.2024.0271.
3. S. Amir, D. F. Priambodo, A. A. Ajhari, and A. Widyasuri, “Analysis of Fraud Attacks Using Android Package Kit in Indonesia,” in 2024 International Conference on Computer, Control, Informatics and its Applications (IC3INA), Bandung, Indonesia, 2024, pp. 285–290, doi: 10.1109/IC3INA64086.2024.10732435. [4] R. Kumar, Exploiting App Differences for Security Analysis of Multi- Geo Mobile Ecosystems, Ph.D. dissertation, 2023.
5.J. K. Nithyanandam et al., “Recognizing the misleading dating app ecosystem,” American Institute of Physics, 2023, doi:
10.1063/5.0164404.
6 Y. Han, S. Wang, Y. Li, X. Cao, L. Huang, and Z. Chen, “Measurement of Illegal Android Gambling App Ecosystem From Joint Promotion Perspective,” in 2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA), Thessaloniki, Greece, 2023, pp. 1–11, doi: 10.1109/DSAA60987.2023.10302499.
7. S. Sebastian and J. Caballero, “Towards attribution in mobile markets: Identifying developer account polymorphism,” in Proc. 2020 ACM SIGSAC Conference on Computer and Communications Security, 2020, pp. 771–785.
8. K. A. Roundy et al., “The many kinds of creepware used for interpersonal attacks,” in 2020 IEEE Symposium on Security and Privacy (SP), San Francisco, CA, USA, 2020, pp. 626–643, doi: 10.1109/SP40000.2020.00069.
9. S. Seraj, M. Pavlidis, M. Trovati, and N. Polatidis, “MadDroid: malicious adware detection in Android using deep learning,” Journal of Cyber Security Technology, vol. 8, no. 3, pp. 163–190, 2023, doi: 10.1080/23742917.2023.2247197.
10. E. Arul and A. Punidha, “Adware attack detection on IoT devices using deep Logistic Regression SVM (DL-SVM-IoT),” in Congress on Intelligent Systems: Proceedings of CIS 2020, vol. 1, pp. 167–176, Springer Singapore, 2021.
11. Z. Chen et al., “Deuedroid: Detecting underground economy apps based on UTG similarity,” in Proc. 32nd ACM SIGSOFT Int. Symp. Softw. Testing Anal., 2023, pp. 223–235.
12. Z. Gu, Z. Xu, H. Chen, J. Lan, C. Meng, and W. Wang, “Mobile user interface element detection via adaptively prompt tuning,” in Proc.IEEE/CVF Conf. Comput. Vis. Pattern Recognit., 2023, pp. 11155– 11164.
13. Y. Hu et al., “Dating with scambots: Understanding the ecosystem of fraudulent dating applications,” IEEE Trans. Dependable Secure Comput., vol. 18, no. 3, pp. 1033–1050, May/Jun. 2021.
14 P. Kotzias, J. Caballero, and L. Bilge, “How did that get in my phone? Unwanted app distribution on Android devices,” in Proc. IEEE Symp. Secur. Privacy, 2021, pp. 53–69.
15 K. A. Roundy et al., “The many kinds of creepware used for interpersonal attacks,” in Proc. IEEE Symp. Secur. Privacy, 2020, pp. 626–643.
16 S. Sebastian and J. Caballero, “Towards attribution in mobile markets: Identifying developer account polymorphism,” in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., 2020, pp. 771–785
Related Articles
2025
Iot-Based Power Theft Detector
2025
Comparative Analysis of Conventional and Diagrid Structural Buildings with Plan Irregularity
2025
The Role of C Language in Google, Adobe, and Mozilla Firefox Applications: Performance, Security, and Future Developments
2025
Seismic Analysis of Circular Building and Rectangular Building
2025
Seismic analysis of double-decker elevated water tank
2025
