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Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM)

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2019
Salima Benqdara

Salima Benqdara. Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM). International Journal of Computer Applications 181(36):10-15, January 2019. BibTeX

	author = {Salima Benqdara},
	title = {Anomaly Intrusion Detection based on a Hybrid Classification Algorithm (GSVM)},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2019},
	volume = {181},
	number = {36},
	month = {Jan},
	year = {2019},
	issn = {0975-8887},
	pages = {10-15},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2019918324},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


One of the major problems in support vector machines (SVM) is the selection of optimal parameters that can establish an efficient SVM to achieve better output with an acceptable level of accuracy. In this paper, proposed a hybrid classification algorithm (GSVM) based Gravitational Search Algorithm (GSA) and support vector machines (SVM) to optimize the accuracy of the SVM classifier by detecting the subset of the best values of the kernel parameters for the SVM classifier. In the GSVM classifier, the GSA is introduced as an optimization technique to optimize the SVM parameters. The GSVM algorithm evaluated using KDD CUP 99 data set and compared to the outperformance of the original SVM algorithms. The results show that the performance of GSVM algorithm has a higher detection rate with lower false positive rate.


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Network Intrusion Detection, ensemble clusters, unlabeled data.