An abnormal-based approach to effectively detect DDOS attacks Li, Ke and Zhou, Wanlei 2009, An abnormal-based approach to effectively detect DDOS attacks, Journal of the Chinese institute of engineers, vol. 32, no. 7, pp. 889-895. Attached Files Name Description MIMEType Size Downloads Title An abnormal-based approach to effectively detect DDOS attacks Author(s) Li, Ke Zhou, Wanlei Journal name Journal of the Chinese institute of engineers Volume number 32 Issue number 7 Start page 889 End page 895 Total pages 7 Publisher Chinese Institute of Engineers Place of publication Taipei City Taiwan, Republic of China Publication date 2009 ISSN 0253-3839 Keyword(s) DDoS Generalized entropy Attacks detection Summary Distributed Denial-of-Service (DDoS) attacks are a serious threat to the safety and security of cyberspace. In this paper we propose a novel metric to detect DDoS attacks in the Internet. More precisely, we use the function of order α of the generalized (Rényi) entropy to distinguish DDoS attacks traffic from legitimate network traffic effectively. In information theory, entropies make up the basis for distance and divergence measures among various probability densities. We design our abnormal-based detection metric using the generalized entropy. The experimental results show that our proposed approach can not only detect DDoS attacks early (it can detect attacks one hop earlier than using the Shannon metric while order  α =2, and two hops earlier than the Shannon metric while order α =10.) but can also reduce both the false positive rate and the false negative rate, compared with the traditional Shannon entropy metric approach. Language eng Field of Research 080503 Networking and Communications Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal HERDC collection year 2009 Persistent URL http://hdl.handle.net/10536/DRO/DU:30028697 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Wed, 26 May 2010, 15:06:35 EST Wed, 26 May 2010, 15:08:03 EST Thu, 27 May 2010, 14:31:19 EST Thu, 27 May 2010, 14:33:35 EST Thu, 27 May 2010, 14:40:18 EST Thu, 27 May 2010, 15:33:29 EST Fri, 28 May 2010, 09:23:19 EST Fri, 28 May 2010, 09:24:20 EST Fri, 16 Jul 2010, 12:22:34 EST Fri, 17 Oct 2014, 10:18:51 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 936 Abstract Views, 5 File Downloads  -  Detailed Statistics Created: Wed, 26 May 2010, 15:06:28 EST by Sandra Dunoon