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Android Adware Detection Using Machine Learning
Journal article   Peer reviewed

Android Adware Detection Using Machine Learning

Sikha Bagui and Daniel Benson
International journal of cyber research and education (Print), Vol.3(2)
07/2021
DOI: https://doi.org/10.4018/IJCRE.2021070101

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Abstract

Adware, an advertising-supported software, becomes a type of malware when it automatically delivers unwanted advertisements to an infected device, steals user information, and opens other vulnerabilities that allow other malware and adware to be installed. With the rise of more and complex evasive malware, specifically adware, better methods of detecting adware are required. Though a lot of work has been done on malware detection in general, very little focus has been put on the adware family. The novelty of this paper lies in analyzing the individual adware families. To date, no work has been done on analyzing the individual adware families. In this paper, using the CICAndMal2017 dataset, feature selection is performed using information gain, and classification is performed using machine learning. The best attributes for classification of each of the individual adware families using network traffic samples are presented. The results present an average classification rate that is an improvement over previous works for classification of individual adware families.

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