Open Access

ARTICLE

Understanding Research Trends in Android Malware Research Using Information Modelling Techniques

by Jaiteg Singh¹, Tanya Gera¹, Farman Ali², Deepak Thakur¹, Karamjeet Singh³, Kyung-sup Kwak^4,*

1 Chitkara University Institute of Engineering & Technology, Chitkara University, Punjab, 140401, India
2 Department of Software, Sejong University, Seoul, 05006, South Korea
3 Department of Computer Science and Engineering, Thapar University, Patiala, 147001, India
4 Department of Information & Communication Engineering, Inha University, Incheon, 22212, South Korea

* Corresponding Author: Kyung-sup Kwak. Email:

(This article belongs to the Special Issue: Machine Learning-based Intelligent Systems: Theories, Algorithms, and Applications)

Computers, Materials & Continua 2021, 66(3), 2655-2670. https://doi.org/10.32604/cmc.2021.014504

Received 24 September 2020; Accepted 12 October 2020; Issue published 28 December 2020

Abstract

Android has been dominating the smartphone market for more than a decade and has managed to capture 87.8% of the market share. Such popularity of Android has drawn the attention of cybercriminals and malware developers. The malicious applications can steal sensitive information like contacts, read personal messages, record calls, send messages to premium-rate numbers, cause financial loss, gain access to the gallery and can access the user’s geographic location. Numerous surveys on Android security have primarily focused on types of malware attack, their propagation, and techniques to mitigate them. To the best of our knowledge, Android malware literature has never been explored using information modelling techniques. Further, promulgation of contemporary research trends in Android malware research has never been done from semantic point of view. This paper intends to identify intellectual core from Android malware literature using Latent Semantic Analysis (LSA). An extensive corpus of 843 articles on Android malware and security, published during 2009–2019, were processed using LSA. Subsequently, the truncated singular Value Decomposition (SVD) technique was used for dimensionality reduction. Later, machine learning methods were deployed to effectively segregate prominent topic solutions with minimal bias. Apropos to observed term and document loading matrix values, this five core research areas and twenty research trends were identified. Further, potential future research directions have been detailed to offer a quick reference for information scientists. The study concludes to the fact that Android security is crucial for pervasive Android devices. Static analysis is the most widely investigated core area within Android security research and is expected to remain in trend in near future. Research trends indicate the need for a faster yet effective model to detect Android applications causing obfuscation, financial attacks and stealing user information.

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