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TL‐GNN: Android Malware Detection Using Transfer Learning

Authors :
Ali Raza
Zahid Hussain Qaisar
Naeem Aslam
Muhammad Faheem
Muhammad Waqar Ashraf
Muhammad Naman Chaudhry
Source :
Applied AI Letters, Vol 5, Iss 3, Pp n/a-n/a (2024)
Publication Year :
2024
Publisher :
Wiley, 2024.

Abstract

ABSTRACT Malware growth has accelerated due to the widespread use of Android applications. Android smartphone attacks have increased due to the widespread use of these devices. While deep learning models offer high efficiency and accuracy, training them on large and complex datasets is computationally expensive. Hence, a method that effectively detects new malware variants at a low computational cost is required. A transfer learning method to detect Android malware is proposed in this research. Because of transferring known features from a source model that has been trained to a target model, the transfer learning approach reduces the need for new training data and minimizes the need for huge amounts of computational power. We performed many experiments on 1.2 million Android application samples for performance evaluation. In addition, we evaluated how well our framework performed in comparison with traditional deep learning and standard machine learning models. In comparison with state‐of‐the‐art Android malware detection methods, the proposed framework offers improved classification accuracy of 98.87%, a precision of 99.55%, recall of 97.30%, F1‐measure of 99.42%, and a quicker detection rate of 5.14 ms using the transfer learning strategy.

Details

Language :
English
ISSN :
26895595
Volume :
5
Issue :
3
Database :
Directory of Open Access Journals
Journal :
Applied AI Letters
Publication Type :
Academic Journal
Accession number :
edsdoj.10bc7096e7104866b1144ee68f0d13cc
Document Type :
article
Full Text :
https://doi.org/10.1002/ail2.94