Advanced Android Malware Detection through Deep Learning Optimization


  • Ahmed Alhussen Department of Computer Engineering, College of Computer and Information Sciences, Majmaah University, Saudi Arabia
Volume: 14 | Issue: 3 | Pages: 14552-14557 | June 2024 |


Android stands out as one of the most prevalent mobile operating systems globally, due to its widespread adoption and open-source nature. However, its susceptibility to malware attacks, facilitated by the ability to install third-party applications without centralized control, poses significant security challenges. Despite efforts to integrate security measures, the proliferation of malicious activities and vulnerabilities emphasizes the need for advanced detection techniques. This study implemented and optimized Long Short-Term Memory (LSTM) and Neural Network (NN) models for malware detection on the Android platform. Leveraging meticulous hyperparameter tuning and robust data preprocessing techniques, this study aimed to increase the efficacy of LSTM and NN models in identifying and mitigating various forms of malware. The results demonstrate remarkable performance, with the LSTM model achieving an accuracy of 99.24%, precision of 99.07%, recall of 98.79%, and F1-score of 98.93%, and the NN model attaining an accuracy of 99.18%, precision of 99.02%, recall of 98.84%, and F1-score of 98.93%. By addressing these challenges and achieving such high levels of accuracy and effectiveness, this study contributes significantly to the ongoing endeavor to fortify defenses against cyber threats, thus fostering a safer digital environment for users worldwide.


LSTM, deep learning, hyperparameter tuning, Android malware


Download data is not yet available.


C. S. Yadav et al., "Malware Analysis in IoT & Android Systems with Defensive Mechanism," Electronics, vol. 11, no. 15, Jan. 2022, Art. no. 2354.

A. Al-Marghilani, "Comprehensive Analysis of IoT Malware Evasion Techniques," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7495–7500, Aug. 2021.

D. Cao et al., "DroidCollector: A High Performance Framework for High Quality Android Traffic Collection," in 2016 IEEE Trustcom/BigDataSE/ISPA, Tianjin, China, Aug. 2016, pp. 1753–1758.

T. Gueye, Y. Wang, M. Rehman, R. T. Mushtaq, and A. Hassan, "Machine Learning for Control Systems Security of Industrial Robots: a Post-covid-19 Overview." Sep. 06, 2022.

C. C. U. López, J. S. D. Villarreal, A. F. P. Belalcazar, A. N. Cadavid, and J. G. D. Cely, "Features to Detect Android Malware," in 2018 IEEE Colombian Conference on Communications and Computing (COLCOM), Medellin, Colombia, May 2018, pp. 1–6.

L. Arora and K. Kumar, "Android Ransomware Detection Toolkit," in 2022 4th International Conference on Artificial Intelligence and Speech Technology (AIST), Delhi, India, Dec. 2022, pp. 1–5.

N. J. Ratyal, M. Khadam, and M. Aleem, "On the Evaluation of the Machine Learning Based Hybrid Approach for Android Malware Detection," in 2019 22nd International Multitopic Conference (INMIC), Islamabad, Pakistan, Aug. 2019, pp. 1–8.

M. Woźniak, J. Siłka, M. Wieczorek, and M. Alrashoud, "Recurrent Neural Network Model for IoT and Networking Malware Threat Detection," IEEE Transactions on Industrial Informatics, vol. 17, no. 8, pp. 5583–5594, Dec. 2021.

D. Arp, M. Spreitzenbarth, M. Hübner, H. Gascon, and K. Rieck, "Drebin: Effective and Explainable Detection of Android Malware in Your Pocket," in Proceedings 2014 Network and Distributed System Security Symposium, San Diego, CA, USA, 2014.

H. Zhang, S. Luo, Y. Zhang, and L. Pan, "An Efficient Android Malware Detection System Based on Method-Level Behavioral Semantic Analysis," IEEE Access, vol. 7, pp. 69246–69256, 2019.

S. Y. Yerima and S. Sezer, "DroidFusion: A Novel Multilevel Classifier Fusion Approach for Android Malware Detection," IEEE Transactions on Cybernetics, vol. 49, no. 2, pp. 453–466, Oct. 2019.

K. Aldriwish, "A Deep Learning Approach for Malware and Software Piracy Threat Detection," Engineering, Technology & Applied Science Research, vol. 11, no. 6, pp. 7757–7762, Dec. 2021.

J. Kumar and G. Ranganathan, "Malware Attack Detection in Large Scale Networks using the Ensemble Deep Restricted Boltzmann Machine," Engineering, Technology & Applied Science Research, vol. 13, no. 5, pp. 11773–11778, Oct. 2023.

M. A. Haq, "Smotednn: A novel model for air pollution forecasting and aqi classification," Computers, Materials and Continua, vol. 71, no. 1, pp. 1403–1425, 2022.

S. Merugu, K. Jain, A. Mittal, and B. Raman, "Sub-scene Target Detection and Recognition Using Deep Learning Convolution Neural Networks," in ICDSMLA 2019, 2020, pp. 1082–1101.

A. Bathula, S. Muhuri, S. kr. Gupta, and S. Merugu, "Secure certificate sharing based on Blockchain framework for online education," Multimedia Tools and Applications, vol. 82, no. 11, pp. 16479–16500, May 2023.

M. Suresh, A. S. Shaik, B. Premalatha, V. A. Narayana, and G. Ghinea, "Intelligent & Smart Navigation System for Visually Impaired Friends," in Advanced Computing, 2023, pp. 374–383.

S. Merugu, M. C. S. Reddy, E. Goyal, and L. Piplani, "Text Message Classification Using Supervised Machine Learning Algorithms," in ICCCE 2018, 2019, pp. 141–150.


How to Cite

A. Alhussen, “Advanced Android Malware Detection through Deep Learning Optimization”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14552–14557, Jun. 2024.


Abstract Views: 123
PDF Downloads: 62

Metrics Information