Detection of Android Malware using Machine Learning and Deep Learning Review
Kiran K. Joshi
Kiran K. Joshi*, Ph.D. Student, Department of Computer Engineering & IT, VJTI, Mumbai (Maharashtra), India.
Manuscript received on 23 April 2022. | Revised Manuscript received on 30 April 2022. | Manuscript published on 30 May 2022. | PP: 134-139 | Volume-11 Issue-1, May 2022. | Retrieval Number: 100.1/ijrte.A69630511122 | DOI: 10.35940/ijrte.A6963.0511122
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Android apps are fast evolving throughout the mobile ecosystem, yet Android malware is always appearing. Various researchers have looked at the issue related with detection of Android malware and proposed hypothesis and approaches from various angles. According to existing studies, machine learning and deep learning seems to be an effective and promising method for detecting Android malware. Despite this, machine learning is used to detect Android malware from various angles. By evaluating a broader variety of facets of the issue, the review work complements prior evaluations. The review process undertakes a systematic literature review to discuss a number of machine learning and deep learning technology that might be used to detect and prevent Android malware from infecting mobile devices. This is a strategy to cope with the rising threat of malware in the Android apps.
Keywords: Android, Malware, Machine Learning, Deep Learning
Scope of the Article: Machine Learning