Using DBSCAN clustering algorithm in detecting DDoS attack
Joint Authors
Ali, Zahrah Muhammad
al-Mamuri, Safa O.
Source
Journal of Babylon University : Journal of Applied and Pure Sciences
Issue
Vol. 23, Issue 4 (31 Dec. 2015), pp.1412-1424, 13 p.
Publisher
Publication Date
2015-12-31
Country of Publication
Iraq
No. of Pages
13
Main Subjects
Information Technology and Computer Science
Abstract EN
Distributed Denial of Service (DDoS) attack, has become one of the major threats to the Internet.
It makes a victim to deny providing normal services in the Internet by generate huge useless packets by a large number of agents and can easily exhaust the computing and communication resources of a victim .In this paper we develop method to detect DDoS attacks accurately and proactively.
This can be achieved using entropy concept to measure abnormal change in traffic according to the phases of the attack , and then these traffics are clustered using DBSCAN algorithm.
The patterns for DDoS traffic is created based on extracted centroid points from each cluster, which are used in testing phase using Distances-based classification .
This system is characterized processing and analyzing of high-speed network traffic (based on entropy approach ), discovering and accurately identifying new types of DDoS attack to reduce the false alarms (FA) , detecting this attack in real time and making use of pattern in the train stage to increase detection ratio.
American Psychological Association (APA)
al-Mamuri, Safa O.& Ali, Zahrah Muhammad. 2015. Using DBSCAN clustering algorithm in detecting DDoS attack. Journal of Babylon University : Journal of Applied and Pure Sciences،Vol. 23, no. 4, pp.1412-1424.
https://search.emarefa.net/detail/BIM-696945
Modern Language Association (MLA)
al-Mamuri, Safa O.& Ali, Zahrah Muhammad. Using DBSCAN clustering algorithm in detecting DDoS attack. Journal of Babylon University : Journal of Applied and Pure Sciences Vol. 23, no. 4 (2015), pp.1412-1424.
https://search.emarefa.net/detail/BIM-696945
American Medical Association (AMA)
al-Mamuri, Safa O.& Ali, Zahrah Muhammad. Using DBSCAN clustering algorithm in detecting DDoS attack. Journal of Babylon University : Journal of Applied and Pure Sciences. 2015. Vol. 23, no. 4, pp.1412-1424.
https://search.emarefa.net/detail/BIM-696945
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p.1424
Record ID
BIM-696945