Android Malware Detection using Machine Learning
Atika Gupta1, Sudhanshu Maurya2, Divya Kapil3, Nidhi Mehra4, Harendra Singh Negi5

1Atika Gupta, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
2Dr Sudhanshu Maurya, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
3Divya Kapil, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
4Nidhi Mehra, Department of Computing, Graphic Era Hill University, (Uttarakhand), India.
5Harendra Singh Negi, Department of Computer Application, Graphic Era Deemed to be University, (Uttarakhand), India.
Manuscript received on 16 June 2019 | Revised Manuscript received on 23 June 2019 | Manuscript Published on 01 July 2020 | PP: 65-70 | Volume-8 Issue-2S12 September 2019 | Retrieval Number: B10110982S1219/2020©BEIESP | DOI: 10.35940/ijrte.B1011.0982S1219
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Abstract: Machine Learning is empowering many aspects of day-to-day lives from filtering the content on social networks to suggestions of products that we may be looking for. This technology focuses on taking objects as image input to find new observations or show items based on user interest. The major discussion here is the Machine Learning techniques where we use supervised learning where the computer learns by the input data/training data and predict result based on experience. We also discuss the machine learning algorithms: Naïve Bayes Classifier, K-Nearest Neighbor, Random Forest, Decision Tress, Boosted Trees, Support Vector Machine, and use these classifiers on a dataset Malgenome and Drebin which are the Android Malware Dataset. Android is an operating system that is gaining popularity these days and with a rise in demand of these devices the rise in Android Malware. The traditional techniques methods which were used to detect malware was unable to detect unknown applications. We have run this dataset on different machine learning classifiers and have recorded the results. The experiment result provides a comparative analysis that is based on performance, accuracy, and cost.
Keywords: Android, Malware, Machine Learning, Classifiers.
Scope of the Article: Machine Learning