MADRAS-NET: A deep learning approach for detecting and classifying android malware using Linknet

Abstract

Malware is an intentionally created malicious software that still poses a serious threat in cyberspace. Android malware has become one of the most significant online threats in recent years due to its increase in prevalence. Even though a lot of work has gone into identifying and categorizing Android malware, problems including data theft, difficulties identifying Android malware, and time-consuming detection remain unresolved. To overcome this issue, this paper proposed a Deep Learning (DL) Based Malware Attack DetectoR in Android Smartphones using LinkNET (MADRAS-NET) which effectively detects and mitigates the types of malwares in Android devices. The Max Abs Scaler receives a set of data as input first for pre-processing. Following pre-processing, the output is sent to LinkNET for classification. LinkNET uses the pre-processed data to identify malware, and then divides the output into three groups: real users, Penetho malware, and FakeAV malware. Lastly, the AndMal2020 dataset, which detects and classifies malware as well as malware families, is used to evaluate the suggested MADRAS-NET approach. The suggested LinkNET achieves a maximum accuracy of 99.81%, while other models, such as the Deep Belief Network (DBN) at 96.75%, the Generative Adversarial Network (GAN) at 94.42%, and the Long Short Term Memory Network (LSTM) at 93.11%, achieve similar results. The comparison of the MADRAS-NET model with the LSTM, GAN, and DBN frameworks indicates its better performance.