Abstract
For researchers and academics, malware detection on smartphones becomes a difficult problem. A half a million distinct Android apps from various Android categories have been gathered to address this issue. The malware detection framework “PARUDroid” is proposed in the current research study by taking into account the features of an app’s rating, API calls, permissions granted, and users who download the app. To select significant features, four different feature selection techniques are implemented on 1844 unique extracted feature datasets. The model is developed by using an ensemble learning approach. The experimental finding is that 98.8% of malware-infected apps may be found using the malware detection model developed using Rough Set Analysis (RSA).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
Notes
https://play.google.com/store/apps are represented by the collected apps.
References
Arp D, Spreitzenbarth M, Hubner M, Gascon H, Rieck K. DREBIN: effective and explainable detection of android malware in your pocket. NDSS. 2014;14:23–6.
Cai H, Meng N, Ryder B, Yao D. DroidCat: effective android malware detection and categorization via app-level profiling. IEEE Trans Inf Forensics Secur. 2018;14(6):1455–70.
Faruki P, Ganmoor V, Laxmi V, Gaur MS, Bharmal A. AndroSimilar: robust statistical feature signature for android malware detection. In: Proceedings of the 6th international conference on security of information and networks. 2013. pp. 152–159.
Kadir AFAb, Stakhanova N, Ghorbani AA. Android Botnets: what URLs are telling us. In: International conference on network and system security. Springer; 2015. pp. 78–91.
Ma Z, Ge H, Wang Z, Liu Y, Liu X. Droidetec: Android malware detection and malicious code localization through deep learning. 2020; arXiv preprint: arXiv:2002.03594.
Mahindru A. Anndroid: a framework for android malware detection using feature selection techniques and machine learning algorithms. In: Mobile application development: practice and experience: 12th industry symposium in conjunction with 18th ICDCIT 2022. Springer; 2023. pp. 47–69.
Mahindru A, Arora H. DNNdroid: Android malware detection framework based on federated learning and edge computing. In: Advancements in smart computing and information security: first international conference, ASCIS 2022, Rajkot, India, November 24–26, 2022, Revised Selected Papers, Part II. Springer; 2023. pp. 96–107.
Mahindru A, Sangal AL. PARUDroid: validation of android malware detection dataset. J Cybersecur Inf Manag. 2020;3(02):42–52.
Mahindru A, Sangal AL. Feature-based semi-supervised learning to detect malware from android. In: Automated software engineering: a deep learning-based approach. Springer; 2020. pp. 93–118.
Mahindru A, Sangal AL. PerbDroid: effective malware detection model developed using machine learning classification techniques. In: A journey towards bio-inspired techniques in software engineering. Springer; 2020. pp. 103–139.
Mahindru A, Sangal AL. HybriDroid: an empirical analysis on effective malware detection model developed using ensemble methods. J Supercomput. 2021;77(8):8209–51.
Mahindru A, Singh P. Dynamic permissions based android malware detection using machine learning techniques. In: Proceedings of the 10th innovations in software engineering conference. 2017. pp. 202–210.
Shabtai A, Kanonov U, Elovici Y, Glezer C, Weiss Y. Andromaly: a behavioral malware detection framework for android devices. J Intell Inf Syst. 2012;38(1):161–90.
Shen F, Vecchio JD, Mohaisen A, Ko SY, Ziarek L. Android malware detection using complex-flows. IEEE Trans Mob Comput. 2018;18(6):1231–45.
Wang W, Zhao M, Wang J. Effective android malware detection with a hybrid model based on deep autoencoder and convolutional neural network. J Ambient Intell Human Comput. 2019;10(8):3035–43.
Yadav P, Menon N, Ravi V, Vishvanathan S, Pham TD. EfficientNet convolutional neural networks-based android malware detection. Comput Secur. 2022;115:102622.
Zheng M, Sun M, Lui JCS. Droid analytics: a signature based analytic system to collect, extract, analyze and associate android malware. In: 2013 12th IEEE international conference on trust, security and privacy in computing and communications. IEEE; 2013. pp. 163–171.
Zhou Y, Jiang X. Dissecting android malware: Characterization and evolution. In: 2012 IEEE symposium on security and privacy. IEEE; 2012. pp. 95–109.
Zhu H-J, You Z-H, Zhu Z-X, Shi W-L, Chen X, Cheng L. DroidDet: effective and robust detection of android malware using static analysis along with rotation forest model. Neurocomputing. 2018;272:638–46.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article is part of the topical collection “Soft Computing Solutions for Secured & Smart Applications” guest edited by Sridaran Rajagopal and Kalpesh Popat.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Mahindru, A., Arora, H. PARUDroid: Framework that Enhances Smartphone Security Using an Ensemble Learning Approach. SN COMPUT. SCI. 4, 630 (2023). https://doi.org/10.1007/s42979-023-02000-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42979-023-02000-y