Improving capabilities of effective android malware detection through Machine learning techniques

Computer Engineering

Now a days, drastically increased android smartphone users. Mobile apps become part of our everyday life, many of the services are provided to us through mobile apps. Therefore Android devices are the most targeted devices by malware because of their high popularity. So that it is necessary to provide Security to android device. To protect our data from malicious software, we need to detect malware from devices. Malware detection is process of detecting an unknown software and application from android device and giving security to the device. There are various ways to attack malicious program on device and previously harmful software can be detect by antivirus software but some traditional anti-virus systems are fail to classify unknown malware into their corresponding families and to detect new kinds of malware programs. So malware detection is necessary to detect malware and protect data. In this paper, we analyze the different malware detection techniques and their comparative studies. To improve malware detection accuracy, Hybrid analysis of malware and combining Boosting and decision tree algorithm used in proposed model.

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