Date of Award
Master of Science
Johnny S. Wong
New security threats emerge against mobile devices as the devices' computing power and storage capabilities evolve. Preventive mechanisms like authentication, encryption alone are not sufficient to provide adequate security for a system. There is a definite need for Intrusion detection systems that will improve security and use fewer resources on the mobile phone. In this work we proposed an intrusion detection method that efficiently detects intrusions in mobile phones using Data Mining techniques. We used network based approach that will remove the overhead processing from the mobile phones. A neural network classifier will be built and trained for each user based on his call logs .An application that runs on smart phone of the user collects certain information of the user and sends them over to the remote server. These logs then fed to the already trained classifier which analyzes the logs and sends back the feedback to the smart phones whenever abnormalities are found. Also we compared different neural classifiers to identify the classifier with better performance. Our results showed clearly the effectiveness of our method to detect intrusions and outperformed existing Intrusion detection methods with 95% detection rate.
Bharat Kumar Addagada
Addagada, Bharat Kumar, "Intrusion Detection in Mobile Phone Systems Using Data Mining Techniques" (2010). Graduate Theses and Dissertations. 11791.