A novel feature-based framework enabling multi-type DDoS attacks detection Zhou, Lu, Zhu, Ye, Xiang, Yong and Zong, T 2022, A novel feature-based framework enabling multi-type DDoS attacks detection, World Wide Web, pp. 1-23, doi: 10.1007/s11280-022-01040-3. Attached Files Name Description MIMEType Size Downloads Title A novel feature-based framework enabling multi-type DDoS attacks detection Author(s) Zhou, Lu orcid.org/0000-0003-4776-4932 Zhu, Ye orcid.org/0000-0003-3545-7863 Xiang, Yong Zong, T Journal name World Wide Web Start page 1 End page 23 Total pages 23 Publisher Springer Place of publication Berlin, Germany Publication date 2022-04-05 ISSN 1386-145X 1573-1413 Keyword(s) ALGORITHMS CLASSIFICATION Communication system security Computer networks Computer Science Computer Science, Information Systems Computer Science, Software Engineering DDoS attacks Machine learning Science & Technology SYSTEM Technology Summary AbstractDistributed Denial of Service (DDoS) attacks are among the most severe threats in cyberspace. The existing methods are only designed to decide whether certain types of DDoS attacks are ongoing. As a result, they cannot detect other types of attacks, not to mention the even more challenging mixed DDoS attacks. In this paper, we comprehensively analyzed the characteristics of various types of DDoS attacks and innovatively proposed five new features from heterogeneous packets including entropy rate of IP source flow, entropy rate of flow, entropy of packet size, entropy rate of packet size, and number of ICMP destination unreachable packet to detect not only various types of DDoS attacks, but also the mixture of them. The experimental results show that the proposed fives features ranked at the top compared with other common features in terms of effectiveness. Besides, by using these features, our proposed framework outperforms the existing methods when detecting various DDoS attacks and mixed DDoS attacks. The detection accuracy improvements over the existing methods are between 21% and 53%. Language eng DOI 10.1007/s11280-022-01040-3 Indigenous content off Field of Research 0804 Data Format 0805 Distributed Computing 0806 Information Systems HERDC Research category C1 Refereed article in a scholarly journal Free to Read? Yes Persistent URL http://hdl.handle.net/10536/DRO/DU:30166508 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)   Connect to Elements publication management system Go to link with your DU access privileges   Link to full text (Open 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. Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au. Versions Version Filter Type Wed, 06 Apr 2022, 08:50:24 EST Wed, 06 Apr 2022, 16:18:12 EST Wed, 06 Apr 2022, 16:19:31 EST Wed, 06 Apr 2022, 16:25:47 EST Thu, 14 Apr 2022, 10:32:44 EST Mon, 02 May 2022, 15:01:15 EST Mon, 02 May 2022, 15:02:30 EST Thu, 30 Jun 2022, 08:39:50 EST Thu, 30 Jun 2022, 12:13:55 EST Thu, 30 Jun 2022, 12:17:42 EST Thu, 30 Jun 2022, 13:33:03 EST Fri, 29 Jul 2022, 23:35:06 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 23 Abstract Views, 1 File Downloads  -  Detailed Statistics Created: Wed, 06 Apr 2022, 08:50:24 EST