Citation:

Mani Gopalsamy, 2025. "Securing Android Systems Using Scalable and Lightweight ML for Ransomware Classification and Identifications", ESP Journal of Engineering & Technology Advancements  5(2): 24-34.

Abstract:

The need to secure mobile devices has never been higher than it is now, given the growing danger of Android ransomware assaults. In order to address this issue, this paper suggests a lightweight machine learning (ML) technique that employs Android malware detection using Decision Tree (DT) and Support Vector Machine (SVM) classifiers. In order to maximize model efficiency, the research was conducted using an Android ransomware dataset and approaches including feature selection, under-sampling, and categorical conversion. At 97.24%, 98.50%, and 98.40%, respectively, ransomware detection accuracy, precision, recall, and F1 scores outperform SVM and traditional machine learning models. It demonstrates how lightweight machine learning models are able to identify ransomware threats while still being computationally efficient to execute on Android devices that are resource-constrained. An improvement in detection accuracy and resilience against evolving ransomware variants can be achieved by future research that combines deep learning with real-time adaptive learning processes.

References:

[1] N. P. Hirenkumar Mistry Kumar Shukla, “Transforming Incident Responses , Automating Security Measures , and Revolutionizing Defence Strategies through AI-Powered Cybersecurity,” p. 2024, 2024.

[2] V. Pillai, “Anomaly Detection for Innovators: Transforming Data into Breakthroughs,” Lib. Media Priv. Ltd., 2022.

[3] S. Duary, P. Choudhury, S. Mishra, V. Sharma, D. D. Rao, and A. Paul Aderemi, “Cybersecurity Threats Detection in Intelligent Networks using Predictive Analytics Approaches,” 4th Int. Conf. Innov. Pract. Technol. Manag. 2024, ICIPTM 2024, p. 364, 2024, doi: 10.1109/ICIPTM59628.2024.10563348.

[4] R. Farhan, “An Approach to Android Ransomware Detection Using Deep Learning,” Wasit J. Pure Sci., vol. 3, pp. 90–94, 2024, doi: 10.31185/wjps.325.

[5] S. S. S. Neeli, “Critical Cybersecurity Strategies for Database Protection against Cyber Attacks,” J. Artif. Intell. Mach. Learn. Data Sci., vol. 1, no. 1, p. 5, 2023.

[6] J. Rahul Dattangire, Ruchika Vaidya, Divya Biradar, “Exploring the Tangible Impact of Artificial Intelligence and Machine Learning: Bridging the Gap between Hype and Reality,” 2024 1st Int. Conf. Adv. Comput. Emerg. Technol., pp. 1–6, 2024.

[7] M. K. A Arif, A Khan, “Role of AI in Predicting and Mitigating Threats: A Comprehensive Review,” JURIHUM J. Inov. dan Hum., vol. 2, no. 3, pp. 297–311, 2024.

[8] M. S. Akaash Vishal Hazarika, “Blockchain-based Distributed AI Models: Trust in AI model sharing,” Int. J. Sci. Res. Arch., vol. 13, no. 2, pp. 3493–3498, 2024.

[9] P. M. Rajendra Prasad Sola, Nihar Malali, “Cloud Database Security: Integrating Deep Learning and Machine Learning for Threat Detection and Prevention: 0,” Notion Press, 2025.

[10] P. Piyush, N. S. Gill, P. Gulia, D. D. Rao, Y. Mandiga, and P. K. Pareek, “Systematic Analysis of threats, Machine Learning solutions and Challenges for Securing IoT environment,” J. Cybersecurity Inf. Manag., vol. 14, no. 2, pp. 367–382, 2024, doi: 10.54216/JCIM.140227.

[11] N. Patel, “AI-Enhanced Zero Trust Security Architecture for Hybrid and Multi-Cloud Data Centers: Automating Trust Validation, Threat Detection, and Mitigation,” Int. J. Nov. Trends Innov., vol. 3, no. 1, pp. a13–a18, 2025.

[12] Albin Ahmed, A. Shaahid, F. Alnasser, S. Alfaddagh, S. Binagag, and D. Alqahtani, “Android Ransomware Detection Using Supervised Machine Learning Techniques Based on Traffic Analysis,” Sensors, 2024, doi: 10.3390/s24010189.

[13] M. I. Khan, A. Arif, and A. R. A. Khan, “AI-Driven Threat Detection: A Brief Overview of AI Techniques in Cybersecurity,” BIN Bull. Informatics, vol. 2, no. 2, pp. 248–261, 2024.

[14] K. Khaliq, N. Z. Ab Rahim, K. Hamid, M. Ibrar, U. Ahmad, and M. U. Ullah, “Ransomware Attacks: Tools and Techniques for Detection,” 2nd Int. Conf. Cyber Resilience, ICCR 2024, pp. 2024–2025, 2024, doi: 10.1109/ICCR61006.2024.10532926.

[15] M. A. Elsadig, “Detection of Denial-of-Service Attack in Wireless Sensor Networks: A Lightweight Machine Learning Approach,” IEEE Access, 2023, doi: 10.1109/ACCESS.2023.3303113.

[16] S. Ismail, D. Dawoud, and H. Reza, “A Lightweight Multilayer Machine Learning Detection System for Cyber-attacks in WSN,” in 2022 IEEE 12th Annual Computing and Communication Workshop and Conference, CCWC 2022, 2022. doi: 10.1109/CCWC54503.2022.9720891.

[17] Guerra-Manzanares, “Machine Learning for Android Malware Detection: Mission Accomplished? A Comprehensive Review of Open Challenges and Future Perspectives,” 2024. doi: 10.1016/j.cose.2023.103654.

[18] M. A. Hossain, T. Hasan, F. Ahmed, S. H. Cheragee, M. H. Kanchan, and M. A. Haque, “Towards superior android ransomware detection: An ensemble machine learning perspective,” Cyber Secur. Appl., vol. 3, no. July 2024, p. 100076, 2025, doi: 10.1016/j.csa.2024.100076.

[19] S. Alsoghyer and I. Almomani, “Ransomware detection system for android applications,” Electron., 2019, doi: 10.3390/electronics8080868.

[20] U. Urooj, B. A. S. Al-Rimy, A. Zainal, F. A. Ghaleb, and M. A. Rassam, “Ransomware Detection Using the Dynamic Analysis and Machine Learning: A Survey and Research Directions,” Appl. Sci., 2022, doi: 10.3390/app12010172.

[21] S. Murri, From Raw to Refined: The Art and Science of Data Engineering. Notion Press, 2025.

[22] S. J. Alghamdi, “Classifying High Strength Concrete Mix Design Methods Using Decision Trees,” Materials (Basel)., 2022, doi: 10.3390/ma15051950.

[23] S. Islam et al., “Image Processing and Support Vector Machine (SVM) for Classifying Environmental Stress Symptoms of Pepper Seedlings Grown in a Plant Factory,” Agronomy, vol. 14, no. 9, p. 2043, 2024, doi: 10.3390/agronomy14092043.

[24] Y. H. Rajarshi Tarafdar, “Finding majority for integer elements,” J. Comput. Sci. Coll., vol. 33, no. 5, pp. 187–191, 2018.

[25] Almomani, A. Alkhayer, and W. El-Shafai, “E2E-RDS: Efficient End-to-End Ransomware Detection System Based on Static-Based ML and Vision-Based DL Approaches,” Sensors, 2023, doi: 10.3390/s23094467.

[26] A. Herrera-Silva and M. Hernández-Álvarez, “Dynamic Feature Dataset for Ransomware Detection Using Machine Learning Algorithms,” Sensors, 2023, doi: 10.3390/s23031053.

Keywords:

Android ransomware, machine learning, ransomware detection, Decision Tree, Support Vector Machine, computational efficiency, ransomware variants.