Abstract
This paper tackles some important matters such as the server performance and the URL phishing. Nowadays the system management is a crucial issue and any potential failure needs to be detected quickly and, at the same time, to avoid URL phishing via defining rules in the firewall setting. An empirical study through data mining is conducted covering different prediction techniques. Lastly, some guidelines are provided to emit a critical view about what may happen and how to act immediately.
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Acknowledgments
This work has been partially subsidised by TIN2014-55894-C2-R and TIN2017-88209-C2-R projects of the Spanish Inter-Ministerial Commission of Science and Technology (MICYT), FEDER funds and the P11-TIC-7528 project of the “Junta de Andalucía” (Spain).
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Tallón-Ballesteros, A.J., Fong, S.J., Wong, R.KK. (2019). An Empirical Study on Performance Server Analysis and URL Phishing Prevention to Improve System Management Through Machine Learning. In: Coppola, M., Carlini, E., D’Agostino, D., Altmann, J., Bañares, J. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2018. Lecture Notes in Computer Science(), vol 11113. Springer, Cham. https://doi.org/10.1007/978-3-030-13342-9_17
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