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008			180530b xxu||||| |||| 00| 0 eng d
040			_cIIITMK
100			_aRamakrishnan T (93616029) _914226
245			_aA machine learning approach for detection of android malware based on hybrid analysis
300			_aMSC CS 2016-2018
500			_a Android is an open source Smartphone OS developed by Google. Android is highly targeted by malware apps in recent years. We suggest a hybrid approach which combines the static and dynamic methods for malware detection in Android. In the static analysis, we extracted the information from source code of android application by reverse engineering the application and in dynamic analysis we have traced out the system calls generated by the application by strace utility. This information is further used as the features of a machine learning classifier. We used machine learning classifiers such as Decision Tree, KNN, Logistic regression, Naive bayes, SVC and random forest. With Decision tree classifier we got 58.33% accuracy, whereas KNN, Logistic Regression and Support Vector Classifier have given an accuracy of 66.66%. Random forest and Naive bayes have given a top accuracy of 75%.
502			_bMSC CS _c2016-2018 _dINT _eDr. Tony Thomas
650			_aANDROID _914227
650			_aMALWARE DETECTION _914228
650			_aMACHINE LEARNING _914229
942			_2ddc _cPR
999			_c6095 _d6095