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\begin{document}

\title{TL-GNN: Android Malware Detection Using Transfer Learning}



\author[1]{Ali Raza}%
\author[1]{Zahid Hussain Qaisar}%
\author[1]{Naeem Aslam}%
\author[2]{Muhammad Faheem}%
\author[3]{Muhammad Waqar Ashraf}%
\author[1]{Muhammad Naman Chaudhry}%
\affil[1]{NFC Institute of Engineering \& Technology}%
\affil[2]{Vaasan Yliopisto Teknillinen tiedekunta}%
\affil[3]{Bahauddin Zakariya University}%


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  \date{May 29, 2024}


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\begin{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 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.%
\end{abstract}%



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