Malware detection and identification in Android using the meta-heuristic based neural network
DOI:
https://doi.org/10.29304/jqcsm.2026.18.32831Keywords:
Malware detection, Android, Fruit fly, PCA, MLPAbstract
The objective of this study is to improve the classification accuracy and lower the chances of false positives by fine-tuning the architecture of the Multilayer perceptron (MLP) network. Based on the Tunadromd dataset, Principal Component Analysis (PCA) will be used to reduce the dimensions of the data to find the most important attributes and cut down computing costs. To overcome the issues posed by the conventional "trial-and-error" method of choosing the right parameters, Fruit Fly Optimization Algorithm (FOA) will be employed to calculate the optimum number of hidden neurons in the Multilayer perceptron (MLP). The classification results reveal that the FOA-MLP model yielded an impressive accuracy rate of 98.9%. Additionally, its precision rate for malware and non-malware samples was recorded at 97.7% and 99.2%, respectively. In terms of sensitivity and the total rate of false-detection, this model produced 96.6% and 1.1%, respectively. It is important to note that this method outperformed popular models such as Extra Tree Classifiers and Stacked Ensembles by 1.67%.
Downloads
References
Alawneh, Heba, David Umphress, and Anthony Skjellum. "Android malware detection using neural networks&process control block information." 2019 14th International Conference on Malicious and Unwanted Software (MALWARE). 2019.
Musa, Hawraa O., and Muhanad T. Younis. "Effective Android Malware Detection Using CNN and LSTM Model with GWO-Based Feature Selection." Babylonian Journal of Machine Learning 2026 (2026): 1-22.
Goodfellow, Ian, et al. Deep learning. Vol. 1. No. 2. Cambridge: MIT press, 2016.
Musikawan, Pakarat, et al. "An enhanced deep learning neural network for the detection and identification of android malware." IEEE Internet of Things Journal 10.10 (2022): 8560-8577.
Baker del Aguila, Ryan, et al. "Static malware analysis using low-parameter machine learning models." Computers 13.3 (2024): 59.
Panja, Subir, et al. "An efficient malware detection approach based on machine learning feature influence techniques for resource-constrained devices." IEEE Access 13 (2025): 12647-12665.
Alhogail, Areej, and Rawan Abdulaziz Alharbi. "Effective ml-based android malware detection and categorization." Electronics 14.8 (2025): 1486.
Xu, Ke, et al. "Deeprefiner: Multi-layer android malware detection system applying deep neural networks." 2018 IEEE European Symposium on Security and Privacy (EuroS&P). IEEE, 2018.
Nguyen, Duc V., et al. "Minad: Multi-inputs neural network based on application structure for android malware detection." Peer-to-peer networking and applications 15.1 (2022): 163-177.
Sun, XingPing, et al. "Android Malware Detection using Sequential Convolutional Neural Networks." Journal of Physics: Conference Series. Vol. 1168. No. 6. IOP Publishing, 2019.
Kim, Jinsung, et al. "MAPAS: a practical deep learning-based android malware detection system." International Journal of Information Security 21.4 (2022): 725-738.
Zhang, Yi, Yuexiang Yang, and Xiaolei Wang. "A novel android malware detection approach based on convolutional neural network." Proceedings of the 2nd international conference on cryptography, security and privacy. 2018.
Zhang, Dehua, et al. "Android malware detection based on hypergraph neural networks." Applied Sciences 13.23 (2023): 12629.
Yumlembam, Rahul, et al. "Iot-based android malware detection using graph neural network with adversarial defense." IEEE Internet of Things Journal 10.10 (2022): 8432-8444.
Wajahat, Ahsan, et al. "An adaptive semi-supervised deep learning-based framework for the detection of Android malware." Journal of Intelligent&Fuzzy Systems 45.3 (2023): 5141-5157.
Zaidi, Atif Raza, et al. "Deep learning-based detection of android malware using graph convolutional networks (gcn)." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 6.1 (2024): 57-73.
Ye, Yanfang, et al. "AiDroid: When heterogeneous information network marries deep neural network for real-time Android malware detection." arXiv preprint arXiv:1811.01027 (2018).
Negi, Divya, and Sandeep Kumar. "A High-Performance Malware Detection Using Extra Tree Classifier and Logistic Regression on TUNADROMD Dataset." 2025 IEEE International Conference on Computer, Electronics, Electrical Engineering&their Applications (IC2E3). IEEE, 2025.
Ambekar, Namrata Govind, et al. "TabLSTMNet: enhancing android malware classification through integrated attention and explainable AI." Microsystem Technologies 31.3 (2025): 695-713.
Pudke, Priya, and Urvashi Bansal. "A Stacked Ensemble Framework for Android Malware Detection Using Semantic Permission Aggregation and Explainable AI." Security and Privacy 8.6 (2025): e70110.
Han, Jiawei, Micheline Kamber, and Jian Pei. "Data mining: Concepts and." Techniques, Waltham: Morgan Kaufmann Publishers (2012): 2012-13.
Haykin, Simon. Neural networks and learning machines, 3/E. Pearson education india, 2009.
Hastie, T., et al. "An introduction to statistical learning Springer." New York (2013).
Fawcett, Tom. "An introduction to ROC analysis." Pattern recognition letters 27.8 (2006): 861-874.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Huda Faris Mohammed
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
