Aslahi-Shahri, B. M. and Rahmani, R. and Chizari, M. and Maralani, A. and Eslami, M. and Golkar, M. J. and Ebrahimi, A. (2016) A hybrid method consisting of GA and SVM for intrusion detection system. Neural Computing and Applications, 27 (6). pp. 1669-1676. ISSN 0941-0643
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Abstract
In this paper, a hybrid method of support vector machine and genetic algorithm (GA) is proposed and its implementation in intrusion detection problem is explained. The proposed hybrid algorithm is employed in reducing the number of features from 45 to 10. The features are categorized into three priorities using GA algorithm as the highest important is the first priority and the lowest important is placed in the third priority. The feature distribution is done in a way that 4 features are placed in the first priority, 4 features in the second, and 2 features in the third priority. The results reveal that the proposed hybrid algorithm is capable of achieving a true-positive value of 0.973, while the false-positive value is 0.017.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Algorithms, Computer crime, Genetic algorithms, Mercury (metal), Support vector machines, False positive, Feature distribution, GA algorithm, Hybrid algorithms, Hybrid method, Intrusion Detection Systems, Network based systems, True positive, Intrusion detection |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Computing |
| ID Code: | 72286 |
| Deposited By: | Fazli Masari |
| Deposited On: | 23 Nov 2017 06:19 |
| Last Modified: | 23 Nov 2017 06:19 |
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