Abstract
Lately, several episodes of tapping and tracking of mobile phones in Europe including Norway have been revealed, showing the vulnerabilities of both the mobile network and mobile phones. A better protection of the user’s confidentiality and privacy is urgently required. This paper will present an innovative mobile network security system using machine learning. The paper will start with a vulnerability and threat analysis of the evolving mobile network, which is a fusion of mobile wireless technologies and Internet technologies, complemented with the Internet of Things. The main part of the paper will concentrate on clarifying how machine learning can help improving mobile network security. The focus will be on elucidating what makes machine learning superior to other techniques. A special case study on the detection of IMSI Catcher, the fake base station that is used in mobile phone tracking and tapping, will be explained.
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Do, V.T., Engelstad, P., Feng, B., van Do, T. (2016). Strengthening Mobile Network Security Using Machine Learning. In: Younas, M., Awan, I., Kryvinska, N., Strauss, C., Thanh, D. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2016. Lecture Notes in Computer Science(), vol 9847. Springer, Cham. https://doi.org/10.1007/978-3-319-44215-0_14
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DOI: https://doi.org/10.1007/978-3-319-44215-0_14
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