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Improving Intrusion Detection System using PSO and SVM Algorithm

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2020
Shwetamaskare, Shubhadubey

Shwetamaskare and Shubhadubey. Improving Intrusion Detection System using PSO and SVM Algorithm. International Journal of Computer Applications 175(28):7-13, October 2020. BibTeX

	author = {Shwetamaskare and Shubhadubey},
	title = {Improving Intrusion Detection System using PSO and SVM Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {October 2020},
	volume = {175},
	number = {28},
	month = {Oct},
	year = {2020},
	issn = {0975-8887},
	pages = {7-13},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2020920770},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


The new computational requirements are growing every day, and taken advantages of these services. But these networks are not fully secured a significant amount of attacks can be deployed on these networks. Therefore to secure the network from the attackers and malicious activities the proposed work is motivated to deliver enhanced IDS (intrusion detection system). That IDS is a data mining algorithm based technique for classifying the malicious patterns. In order to implement this technique the KDD CUP dataset is used. That dataset contains 41 attributes and 1 class attribute. This huge dimension can impact on the performance of IDS system. Therefore first the data processing technique is used to cleaning the data. After that the PSO (Particle swarm optimization) technique is used. Using this algorithm , rank all the attributes and select the features. The selected features are less in size means it contains 21 attributes and 1 class attribute. In this selected features the SVM algorithm is employed for classifying the data. The experimental results on different size of dataset demonstrate the effective performance of the proposed data model. That is also compared with the relevant k-NN classification model. The comparative performance analysis demonstrate the proposed model is accurate and less time consuming for classification of patterns as compared to the k-NN based model. But the memory usages of the proposed model are higher with respect to the k-NN model.


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IDS, data mining, PSO, SVM, classification, KDD CUP 99’s