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IDEAL
2003
Springer

Detecting Distributed Denial of Service (DDoS) Attacks through Inductive Learning

9 years 2 months ago
Detecting Distributed Denial of Service (DDoS) Attacks through Inductive Learning
As the complexity of Internet is scaled up, it is likely for the Internet resources to be exposed to Distributed Denial of Service (DDoS) flooding attacks on TCP-based Web servers. There has been a lot of related work which focuses on analyzing the pattern of the DDoS attacks to protect users from them. However, none of these studies takes all the flags within TCP header into account, nor do they analyze relationship between the flags and the TCP packets. To analyze the features of the DDoS attacks, therefore, this paper presents a network traffic analysis mechanism which computes the ratio of the number of TCP flags to the total number of TCP packets. Based upon the calculation of TCP flag rates, we compile a pair of the TCP flag rates and the presence (or absence) of the DDoS attack into state-action rules using machine learning algorithms. We endow alarming agents with a tapestry of the compiled rules. The agents can then detect network flooding attacks against a Web server. We vali...
Sanguk Noh, Cheolho Lee, Kyunghee Choi, Gihyun Jun
Added 07 Jul 2010
Updated 07 Jul 2010
Type Conference
Year 2003
Where IDEAL
Authors Sanguk Noh, Cheolho Lee, Kyunghee Choi, Gihyun Jung
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