Version 1
: Received: 14 June 2022 / Approved: 16 June 2022 / Online: 16 June 2022 (11:01:47 CEST)
How to cite:
Mugees Asif, M.; Asif, S.; Mubarik, I.; Hussain, R. Malicious Applications Detection in Android using Machine Learning. Preprints2022, 2022060241. https://doi.org/10.20944/preprints202206.0241.v1
Mugees Asif, M.; Asif, S.; Mubarik, I.; Hussain, R. Malicious Applications Detection in Android using Machine Learning. Preprints 2022, 2022060241. https://doi.org/10.20944/preprints202206.0241.v1
Mugees Asif, M.; Asif, S.; Mubarik, I.; Hussain, R. Malicious Applications Detection in Android using Machine Learning. Preprints2022, 2022060241. https://doi.org/10.20944/preprints202206.0241.v1
APA Style
Mugees Asif, M., Asif, S., Mubarik, I., & Hussain, R. (2022). Malicious Applications Detection in Android using Machine Learning. Preprints. https://doi.org/10.20944/preprints202206.0241.v1
Chicago/Turabian Style
Mugees Asif, M., Iqra Mubarik and Rabia Hussain. 2022 "Malicious Applications Detection in Android using Machine Learning" Preprints. https://doi.org/10.20944/preprints202206.0241.v1
Abstract
A huge number of applications available for Android-based smartphone devices have emerged over the past years. Due to which a huge number of malicious applications has been growing explosively. Many approaches have been proposed to ensure the security and quality of application in the markets. Usually, Machine Learning approaches are utilized in the classification process of malicious application detection. Calculating accurate results of characterizing applications behaviors, or other features, has a direct effect on the results with Machine Learning calculations. Android applications emerge so quickly. The behavior of current applications has gotten progressively malicious. The extraction of malware-infected features from applications is thus become a difficult task. According to our knowledge, a ton of features have been extricated in existing work however no survey has overviewed the features built for identifying malicious applications efficiently. In this paper, we will in general give an extensive review of such sort of work that identifies feature applications by describing various practices of uses with various kinds of features. In this survey we have discussed the following dimensions: extraction and selection of feature methods if any, methods of detection and evaluation performed. In light of our review, we notice the issues of investigating malware-affected features from applications, give the scientific categorization and demonstrate the future headings.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
