Abstract
Security in telemedicine systems might be considered a particularly sensitive subject due to the type of confidential information generally handled and the responsibilities consequently derived. In this work we focus on detecting attempts of gaining unauthorised access to a telemedicine web application. We introduce a new Text Mining module that by using Text Categorisation of the web application server log entries is capable of learning the characteristics of both normal and malicious user behaviour. As a result, the detection of misuse in the web application is achieved without the need of explicit programming hence improving the system maintainability.
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Adeva, J.J.G., Pikatza, J.M., Flórez, S., Sobrado, F.J. (2005). Intrusion Detection Using Text Mining in a Web-Based Telemedicine System. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_131
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DOI: https://doi.org/10.1007/11589990_131
Publisher Name: Springer, Berlin, Heidelberg
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