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Journal of Network and Systems Management
Published in: Journal of Network and Systems Management 1/2022

01-01-2022

Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder

Authors: Samaneh Mahdavifar, Dima Alhadidi, Ali. A. Ghorbani

Published in: Journal of Network and Systems Management | Issue 1/2022

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Abstract

Android has become the target of attackers because of its popularity. The detection of Android mobile malware has become increasingly important due to its significant threat. Supervised machine learning, which has been used to detect Android malware is far from perfect because it requires a significant amount of labeled data. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique, namely pseudo-label stacked auto-encoder (PLSAE), which involves training using a set of labeled and unlabeled instances. We use a hybrid approach of dynamic analysis and static analysis to craft feature vectors. We evaluate our proposed model on CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories. After that, we compare the results with state-of-the-art techniques in terms of accuracy and efficiency. Experimental results show that our proposed framework outperforms other semi-supervised approaches and common machine learning algorithms.
previous article Machine-Learning-Enabled DDoS Attacks Detection in P4 Programmable Networks

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Appendix
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Footnotes
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Metadata
Title
Effective and Efficient Hybrid Android Malware Classification Using Pseudo-Label Stacked Auto-Encoder
Authors
Samaneh Mahdavifar
Dima Alhadidi
Ali. A. Ghorbani
Publication date
01-01-2022
Publisher
Springer US
Published in
Journal of Network and Systems Management / Issue 1/2022
Print ISSN: 1064-7570
Electronic ISSN: 1573-7705
DOI
https://doi.org/10.1007/s10922-021-09634-4

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