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A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning

Manokaran Newlin Rajkumar, Varadhan Venkatesa Kumar, Ramachandhiran Vijayabhasker
Copyright: © 2020 |Pages: 19
ISBN13: 9781522596110|ISBN10: 1522596119|ISBN13 Softcover: 9781522596127|EISBN13: 9781522596134
DOI: 10.4018/978-1-5225-9611-0.ch009
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MLA

Rajkumar, Manokaran Newlin, et al. "A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning." Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, IGI Global, 2020, pp. 176-194. https://doi.org/10.4018/978-1-5225-9611-0.ch009

APA

Rajkumar, M. N., Kumar, V. V., & Vijayabhasker, R. (2020). A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning. In P. Ganapathi & D. Shanmugapriya (Eds.), Handbook of Research on Machine and Deep Learning Applications for Cyber Security (pp. 176-194). IGI Global. https://doi.org/10.4018/978-1-5225-9611-0.ch009

Chicago

Rajkumar, Manokaran Newlin, Varadhan Venkatesa Kumar, and Ramachandhiran Vijayabhasker. "A Hybrid Approach to Detect the Malicious Applications in Android-Based Smartphones Using Deep Learning." In Handbook of Research on Machine and Deep Learning Applications for Cyber Security, edited by Padmavathi Ganapathi and D. Shanmugapriya, 176-194. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-5225-9611-0.ch009

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Abstract

This modern era of technological advancements facilitates the people to possess high-end smart phones with incredible features. With the increase in the number of mobile applications, we are witnessing the humongous increase in the malicious applications. Since most of the Android applications are available open source and used frequently in the smart phones, they are more vulnerable. Statistical and dynamical-based malware detection approaches are available to verify whether the mobile application is a genuine one, but only to a certain extent, as the level of mobile application scanning done by the said approaches are in general routine or a common, pre-specified pattern using the structure of control flow, information flow, API call, etc. A hybrid method based on deep learning methodology is proposed to identify the malicious applications in Android-based smart phones in this chapter, which embeds the possible merits of both the statistical-based malware detection approaches and dynamical-based malware detection approaches and minimizes the demerits of them.

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