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Vol.6, No.4, November 2017. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS 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
Abstract:
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. |
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