Distributed Denial of Service detection using hybrid machine learning technique
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 at...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
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IEEE (IEEE Xplore)
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/39735/ |
| _version_ | 1848849226153328640 |
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| author | Barati, Mehdi Abdullah, Azizol Udzir, Nur Izura Mahmod, Ramlan Mustapha, Norwati |
| author_facet | Barati, Mehdi Abdullah, Azizol Udzir, Nur Izura Mahmod, Ramlan Mustapha, Norwati |
| author_sort | Barati, Mehdi |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-15T09:47:01Z |
| format | Conference or Workshop Item |
| id | upm-39735 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T09:47:01Z |
| publishDate | 2014 |
| publisher | IEEE (IEEE Xplore) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-397352019-12-09T09:05:57Z http://psasir.upm.edu.my/id/eprint/39735/ Distributed Denial of Service detection using hybrid machine learning technique Barati, Mehdi Abdullah, Azizol Udzir, Nur Izura Mahmod, Ramlan Mustapha, Norwati 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. IEEE (IEEE Xplore) 2014 Conference or Workshop Item NonPeerReviewed 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). 10.1109/ISBAST.2014.7013133 |
| spellingShingle | Barati, Mehdi Abdullah, Azizol Udzir, Nur Izura Mahmod, Ramlan Mustapha, Norwati Distributed Denial of Service detection using hybrid machine learning technique |
| title | Distributed Denial of Service detection using hybrid machine learning technique |
| title_full | Distributed Denial of Service detection using hybrid machine learning technique |
| title_fullStr | Distributed Denial of Service detection using hybrid machine learning technique |
| title_full_unstemmed | Distributed Denial of Service detection using hybrid machine learning technique |
| title_short | Distributed Denial of Service detection using hybrid machine learning technique |
| title_sort | distributed denial of service detection using hybrid machine learning technique |
| url | http://psasir.upm.edu.my/id/eprint/39735/ http://psasir.upm.edu.my/id/eprint/39735/ |