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Detection of Malware using Machine Learning in Android Devices/Applications

Chukka, Hanok Vijay (2020) Detection of Malware using Machine Learning in Android Devices/Applications. Masters thesis, Dublin, National College of Ireland.

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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. I will also help stakeholders of Android devices to be safe from malicious software. The proposed system extracts featurefrom. 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.
Keywords – Malware detection, feature extraction, machine learning, SVM, Random Forest, Multinomial Naïve Bayes

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software > Computer Security
T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions: School of Computing > Master of Science in Cyber Security
Depositing User: Dan English
Date Deposited: 26 Jan 2021 15:16
Last Modified: 26 Jan 2021 15:16

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