A symptom-based taxonomy for an early detection of network attacks

Authors:
Ki-Yoon Kim;Hyoung-Kee Choi
Affiliations:
The School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea;The School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea
Venue:
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Year:
2007

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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.