loading page

TL-GNN: Android Malware Detection Using Transfer Learning
  • +3
  • Ali Raza,
  • Zahid Hussain Qaisar,
  • Naeem Aslam,
  • Muhammad Faheem,
  • Muhammad Waqar Ashraf,
  • Muhammad Naman Chaudhry
Ali Raza
NFC Institute of Engineering & Technology
Author Profile
Zahid Hussain Qaisar
NFC Institute of Engineering & Technology
Author Profile
Naeem Aslam
NFC Institute of Engineering & Technology
Author Profile
Muhammad Faheem
Vaasan Yliopisto Teknillinen tiedekunta

Corresponding Author:[email protected]

Author Profile
Muhammad Waqar Ashraf
Bahauddin Zakariya University
Author Profile
Muhammad Naman Chaudhry
NFC Institute of Engineering & Technology
Author Profile


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 data sets 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 to traditional deep learning and standard machine learning models. In comparison to 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 by utilizing the transfer learning strategy.
05 Oct 2023Submitted to Applied AI Letters
06 Oct 2023Assigned to Editor
06 Oct 2023Submission Checks Completed
06 Oct 2023Reviewer(s) Assigned