Android malware analysis using multiple machine learning algorithms
by Rahul Kumar Sahani; Madhusudan Anand; Arhit Bose Tagore; Shreyash Mehrotra; Ruksana Tabassum; S.P. Raja
International Journal of Electronic Security and Digital Forensics (IJESDF), Vol. 16, No. 6, 2024

Abstract: Currently, Android is a booming technology that has occupied the major parts of the market share. However, as Android is an open-source operating system there are possibilities of attacks on the users, there are various types of attacks but one of the most common attacks found was malware. Malware with machine learning (ML) techniques has proven as an impressive result and a useful method for malware detection. Here in this paper, we have focused on the analysis of malware attacks by collecting the dataset for the various types of malware and we trained the model with multiple ML and deep learning (DL) algorithms. We have gathered all the previous knowledge related to malware with its limitations. The machine learning algorithms were having various accuracy levels and the maximum accuracy observed is 99.68%. It also shows which type of algorithm is preferred depending on the dataset. The knowledge from this paper may also guide and act as a reference for future research related to malware detection. We intend to make use of Static Android Activity to analyse malware to mitigate security risks.

Online publication date: Mon, 07-Oct-2024

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