Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning
Android is an open source free operating system and it has support from Google to publish android application on its Play Store. Anybody can developed an android app and publish on play store free of cost. This android feature attract cyber-criminals to developed and publish malware app on play store. If anybody install such malware app then it will steal information from phone and transfer to cyber-criminals or can give total phone control to criminal’s hand. To protect users from such app in this paper author is using machine learning algorithm to detect malware from mobile app.
To detect malware from app we need to extract all code from app using reverse engineering and then check whether app is doing any mischievous activity such as sending SMS or copying contact details without having proper permissions. If such activity given in code then we will detect that app as malicious app. In a single app there could be more than 100 permissions (examples of permissions are transact, API call signature, onServiceConnected, API call signature, bindService, API call signature, attachInterface, API call signature, ServiceConnection, API call signature, android.os.Binder, API call signature, SEND_SMS, Manifest Permission, Ljava.lang.Class.getCanonicalName, API call signature etc.) which we need to extract from code and then generate a features dataset, if app has proper permission then we will put value 1 in the features data and if not then we will value 0. Based on those features dataset app will be mark as malware or good ware.
Android-malware-detection
Android Malware Detection Using Genetic Algorithm based Optimized Feature Selection and Machine Learning
Android is an open source free operating system and it has support from Google to publish android application on its Play Store. Anybody can developed an android app and publish on play store free of cost. This android feature attract cyber-criminals to developed and publish malware app on play store. If anybody install such malware app then it will steal information from phone and transfer to cyber-criminals or can give total phone control to criminal’s hand. To protect users from such app in this paper author is using machine learning algorithm to detect malware from mobile app.
To detect malware from app we need to extract all code from app using reverse engineering and then check whether app is doing any mischievous activity such as sending SMS or copying contact details without having proper permissions. If such activity given in code then we will detect that app as malicious app. In a single app there could be more than 100 permissions (examples of permissions are transact, API call signature, onServiceConnected, API call signature, bindService, API call signature, attachInterface, API call signature, ServiceConnection, API call signature, android.os.Binder, API call signature, SEND_SMS, Manifest Permission, Ljava.lang.Class.getCanonicalName, API call signature etc.) which we need to extract from code and then generate a features dataset, if app has proper permission then we will put value 1 in the features data and if not then we will value 0. Based on those features dataset app will be mark as malware or good ware.