Android Malware detection using machine learning (KEY IJP************188)

• Preeti Shinge ,Rachana Dudam ,Premsai Kalekar,Saleha Landage

Abstract

The popularity of the Android platform in smartphones and other Internet-of-Things devices has resulted in the explosive of malware attacks against it. To fight against the explosive growth of Android malware, we propose a static malware detection framework, known as SEDMDroid. Malware presents a serious threat to the security of devices and the services they provided, e.g. stealing the privacy sensitive data stored in mobile devices.The main aim of the study is to explore the malware prediction in android. Data pre-processing and model selection is the first two faces. In model selection that, the data is divided into two portions, train set and test set, in a ratio of 80% and 20%, respectively. In the third phase Principle Component Analysis is implemented for feature reduction, in classification the most prominent prediction models are implemented, such as Support Vector Machine and Multi-Layer Perceptron algorithms are used to evaluate how they impacted model accuracy. Finally, the predicted result in the form of AUCRUC curve was used to analyse the findings obtained on the test set.