• Goutham Lingampally
• Shiva Krishna Gujju

Spreading malware through Android devices and applications became an important strategy of cyber attackers. Therefore, malware detection in Android applications has become an important area of research. In this context, it is important to answer the question that reads “how can we develop a model based on Machine Learning (ML) to detect malware in Android devices/applications?” When malware is detected in real time from Android mobile applications, it can relive the users of Android phones from the risk of malware. It will also help stakeholders of Android devices to be safe from malicious software. The proposed system extracts feature from. APK files and training is given for supervised learning. Different ML models like Multinomial Naïve Bayes, Random Forest and SVM are used as prediction models. With these ML techniques a framework is realized to have provision for protection of malware in Android devices or applications. The proposed solution continues giving support with increased quality. The rationale behind this is that as the applications are protected and malware is detected, the training data gets increased. With increased training data, it will become much more accurate as time goes on. With some changes, it can be made to detect Android applications live when it is associated with a competing device.

Malware Detection , feature extraction, machine learning, SVM, Random Forest, Multinomial Naïve Bayes

"Detection of Malware using Machine Learning in Android Devices/Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.f30-f37, July-2021, Available :http://www.jetir.org/papers/JETIR2107634.pdf

"Detection of Malware using Machine Learning in Android Devices/Applications", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppf30-f37, July-2021, Available at : http://www.jetir.org/papers/JETIR2107634.pdf