Journal Press India^®

Author Details ( * ) denotes Corresponding author

The rapid growth of Android applications has been accompanied by a surge in malicious apps that exploit system permissions and static components to bypass conventional security defenses. While static analysis has proven useful for early malware detection, many existing approaches fall short due to limited feature representation and a lack of adaptability to evolving threats. This paper introduces GENDroid, a novel framework that enhances Android malware detection by combining multi-modal static feature fusion with evolutionary optimization. GENDroid integrates diverse features—including permissions, API calls, and intent filters—into a unified representation, enabling a deeper understanding of application behavior. To optimize both feature selection and classifier performance, a Genetic Algorithm (GA)-driven strategy is employed, allowing the system to evolve and adapt automatically. The optimized feature set is used to train an ensemble of machine learning classifiers on a comprehensive dataset comprising both real-world and synthetic Android applications. Experimental results demonstrate that GENDroid delivers high detection accuracy, significantly reduces false positives, and remains robust against adversarial variants. Its modular design also allows for the seamless integration of additional static or behavioral features. By intelligently combining feature diversity with adaptive learning, GENDroid provides a practical and scalable solution for Android malware detection, effectively addressing the limitations of traditional static analysis techniques.

Keywords

Android Malware Detection; Static Analysis; Multi-Modal Feature Fusion; Permission-to-Exploitation Mapping; Genetic Algorithms; Feature Selection; Machine Learning; Ensemble Learning; Adversarial Robustness; APK Analysis

1. Alani, M. M. & Awad, A. I. (2022). PAIRED: An explainablelightweightAndroidmalwaredetectionsystem.IEEE Access, 10, 73214–73228. Retrieved from https://doi.org/10.1109/ACCESS.2022.3189645
2. Alkahtani, H. & Aldhyani, T. H. H. (2022). Artificial intelligencealgorithms for malware detection in Android-operated mobiledevices. Sensors, 22(6), 2268. Retrieved from https://doi.org/10.3390/s22062268
3. Almarshad, F. A., Zakariah, M., Gashgari, G. A., Aldakheel,E. A. & Alzahrani, A. I. A. (2023). Detection of Android malware usingmachine learning and Siamese shot learning technique for security.IEEE Access, 11,127697–127714. Retrieved from https://doi.org/10.1109/ACCESS.2023.3331739
4. Liu, Z.,Wang,R.,Japkowicz, N.,Gomes, H.M.,Peng, B.& Zhang, W. (2024). SeGDroid: An Android malware detectionmethod based on sensitive function call graph learning. Expert Systems with Applications, 235, 121125. Retrieved from https://doi.org/10.1016/j.eswa.2023.121125
5. Mahindru, A. & Sangal, A. L. (2021). MLDroid—framework for Android malware detection using machine learning techniques. Neural Computing and Application, 33(10), 5183–5240. Retrieved from https://doi.org/10.1007/s00521-020-05309-4
6. Mehtab, A., Shahid, W. B., Yaqoob, T., Amjad, M. F., Abbas, H., Afzal, H. & Saqib, M.N.AdDroid:Rule-basedmachinelearningframe-work for Android malware analysis. Mobile Network and Applications, 25(1),180–192. Retrieved from https://doi.org/10.1007/ s11036-019-01248-0
7. Odat, E. & Yaseen, Q. M. (2023). A novel machine learning approachfor Android malware detection based on the co-existence offeatures. IEEE Access, 11, 15471–15484. Retrieved from https://doi.org/10.1109/ACCESS.2023.3244656
8. Surendran, R., Thomas, T. & Emmanuel, S. (2020). GSDroid: GraphsignalbasedcompactfeaturerepresentationforAndroidmalwaredetection. Expert Systems with Applications, 159, 113581. Retrieved from https://doi.org/10.1016/j.eswa.2020.113581
9. Vu, L. N. & Jung, S. (2021). AdMat: A CNN-on-matrix approach toAndroidmalwaredetectionandclassification.IEEE Access, 9,39680–39694. Retrieved from https://doi.org/ 10.1109/ACCESS.2021.3063748