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Distributed Denial of Service detection using hybrid machine learning technique


Citation

Barati, Mehdi and Abdullah, Azizol and Udzir, Nur Izura and Mahmod, Ramlan and Mustapha, Norwati (2014) Distributed Denial of Service detection using hybrid machine learning technique. In: 2014 International Symposium on Biometrics and Security Technologies (ISBAST), 26-27 Aug. 2014, Kuala Lumpur, Malaysia. (pp. 268-273).

Abstract / Synopsis

Distributed Denial of Service (DDoS) is a major threat among many security issues. To overcome this problem, many studies have been carried out by researchers, however due to inefficiency of their techniques in terms of accuracy and computational cost, proposing an efficient method to detect DDoS attack is still a hot topic in research. Current paper proposes architecture of a detection system for DDoS attack. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are deployed for feature selection and attack detection respectively in our hybrid method. Wrapper method using GA is deployed to select the most efficient features and then DDoS attack detection rate is improved by applying Multi-Layer Perceptron (MLP) of ANN. Results demonstrate that the proposed method is able to detect DDoS attack with high accuracy and deniable False Alarm.


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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Computer Science and Information Technology
DOI Number: https://doi.org/10.1109/ISBAST.2014.7013133
Publisher: IEEE (IEEE Xplore)
Depositing User: Nursyafinaz Mohd Noh
Date Deposited: 06 Aug 2015 12:43
Last Modified: 06 Aug 2015 12:43
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1109/ISBAST.2014.7013133
URI: http://psasir.upm.edu.my/id/eprint/39735
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