Abstract
We present a symptom-based taxonomy for an early detection of network attacks. Since this taxonomy uses symptoms in the network it is relatively easy to access the information to classify the attack. Accordingly it is quite early to detect an attack as the symptom always appears before the main stage of the attack. Furthermore, we are able to classify unknown attacks if the symptom of unknown attacks is correlated with the one of the already known attacks. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Kim, K. Y., & Choi, H. K. (2007). A symptom-based taxonomy for an early detection of network attacks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4430 LNCS, pp. 327–328). Springer Verlag. https://doi.org/10.1007/978-3-540-71549-8_42
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