- Title
- Detection of android malware using tree-based ensemble stacking model
- Creator
- Shafin, Sakib; Ahmed, Md Maroof; Pranto, Mahmud; Chowdhury, Abdullahi
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/188191
- Identifier
- vital:17244
- Identifier
-
https://doi.org/10.1109/CSDE53843.2021.9718396
- Identifier
- ISBN:9781665495523 (ISBN)
- Abstract
- Increasing use of smartphones for everyday activities from banking, education to social networking is putting our personal information at risk as smartphone operating systems and applications are vulnerable to various types of attacks including malware attack. To this end Android operating system is particularly targeted as it is the most widely used mobile operating system. Building a robust detection system that can provide protection against recent attacks and can deliver not only accurate detection but also the type of the attack in order to protect the system is vital. In this study, we propose a twolayer Machine Learning detection model based on Ensemble Learning and Stacked Generalization method to accurately predict and classify the growing attacks on Android smartphones. We evaluated the proposed model on a very recent dataset, named CIC-Maldroid-2020, which contains 11,598 samples with various malicious attack types. The performance of our proposed model was evaluated on widely used metrics, like accuracy, precision, recall & F1-score. It outperforms previous studies done on the same dataset and achieves an accuracy of 99.49% in classifying each attack type. © IEEE 2022.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 2021 IEEE Asia-Pacific Conference on Computer Science and Data Engineering, CSDE 2021, Brisbane, 8-10 December 2021
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © IEEE 2022
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