Ramakrishnan T (93616029) <br/><br/>
A machine learning approach for detection of android malware based on hybrid analysis 

 - MSC CS 2016-2018 
<br/><br/> <br/>Android is an open source Smartphone OS developed by Google. Android is highly targeted<br/>by malware apps in recent years. We suggest a hybrid approach which combines the static<br/>and dynamic methods for malware detection in Android. In the static analysis, we extracted<br/>the information from source code of android application by reverse engineering the<br/>application and in dynamic analysis we have traced out the system calls generated by the<br/>application by strace utility. This information is further used as the features of a machine<br/>learning classifier. We used machine learning classifiers such as Decision Tree, KNN,<br/>Logistic regression, Naive bayes, SVC and random forest. With Decision tree classifier we <br/>got 58.33% accuracy, whereas KNN, Logistic Regression and Support Vector Classifier have <br/>given an accuracy of 66.66%. Random forest and Naive bayes have given a top accuracy of<br/>75%. <br/><br/><br/><br/> 
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<br/><br/><label>Subjects--Topical Terms: </label><br/>ANDROID<br/>MALWARE DETECTION<br/>MACHINE LEARNING