Vol.6, No.4, November 2017.                                                                                                                                                                   ISSN: 2217-8309

                                                                                                                                                                                                                eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

SYN Flood Attack Detection in Cloud Computing using Support Vector Machine


Zerina Mašetić, Dino Kečo, Nejdet Doǧru, Kemal Hajdarević


© 2017 Murat Tezer, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 6, Issue 4, Pages 752-759, ISSN 2217-8309, DOI: 10.18421/TEM64-15, November 2017.


Received: 09 September 2017
Accepted: 30 October 2017
Published: 27 November 2017




Cloud computing is a trending technology, as it reduces the cost of running a business. However, many companies are skeptic moving about towards cloud due to the security concerns. Based on the Cloud Security Alliance report, Denial of Service (DoS) attacks are among top 12 attacks in the cloud computing. Therefore, it is important to develop a mechanism for detection and prevention of these attacks. The aim of this paper is to evaluate Support Vector Machine (SVM) algorithm in creating the model for classification of DoS attacks and normal network behaviors. The study was performed in several phases: a) attack simulation, b) data collection, c)feature selection, and d) classification. The proposedmodel achieved 100% classification accuracy with true positive rate (TPR) of 100%. SVM showed outstanding performance in DoS attack detection and proves that it serves as a valuable asset in the network security area.


Keywords –Cloud computing, SYN flood, DoS attack, Support Vector Machine.



Full text PDF >  



Copyright © 2012-2017 UIKTEN, All Rights reserved
Copyright licence: All articles are licenced via Creative Commons CC BY-NC-ND 4.0 licence