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Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization

International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 100 - Number 18
Year of Publication: 2014
Sunita Patel
Jyoti Sondhi
Anand Motvani
Anurag Shrivastava

Sunita Patel, Jyoti Sondhi, Anand Motvani and Anurag Shrivastava. Article: Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization. International Journal of Computer Applications 100(18):34-37, August 2014. Full text available. BibTeX

	author = {Sunita Patel and Jyoti Sondhi and Anand Motvani and Anurag Shrivastava},
	title = {Article: Improved Intrusion Detection Technique based on Feature Reduction and Classification using Support Vector Machine and Particle of Swarm Optimization},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {100},
	number = {18},
	pages = {34-37},
	month = {August},
	note = {Full text available}


Reduces the file size and increase the performances of classification and intrusion detection technique used in current research trend. The reduction of file size and number of attribute used dimension reduction algorithm and optimization algorithm. Various authors used genetic algorithm, ANT colony optimization and neural network. In this paper used particle of swarm optimization technique for feature reduction and feature selection for support vector machine classification process. The proposed algorithm implemented in MATLAB software and used DARPA dataset for evaluation of proposed method. Our empirical result shows that better detection ratio in compression of other exiting technique such as FCMNN, GSVM.


  • A M Chandrasekhar, K raghuveer"Intrusion detection technique by using k-means, fuzzy neural network and SVM classifier" International conference on computer communication and informatics, IEEE, 2013. Pp 1-7.
  • V. Bapuji, R. Naveen Kumar, A. Govardhan, S. S. V. N. Sarma "Soft Computing and Artificial Intelligence Techniques for Intrusion Detection System" Network and Complex Systems, Vol-2, 2012. Pp 24-33.
  • IftikharAhmad ,Azween Abdullah ,Abdullah Alghamdi , Muhammad Hussain "Optimized intrusion detection mechanism using soft computing Techniques" Springer 2012. Pp 1-9.
  • Iftikhar Ahmad, Azween Abdullah, Abdullah Alghamdi "Towards the Selection of Best Neural Network System for Intrusion Detection" 2011. Pp 1-8.
  • Bibi Masoomeh Aslahi Shahri, I. R . Adeyami ,Bahareh Maleki Alavi " Intrusion Detection System Using Hybrid Gsa-K-Means" Proceedings of Global Engineering, Science and Technology Conference, 2013. Pp 1-14.
  • ShaohuaTeng, Hongle Du, Naiqi Wu, Wei Zhang, Jiangyi Su "A Cooperative Network Intrusion Detection Based on Fuzzy SVMs" JOURNAL OF NETWORKS, VOL. 5, 2010. Pp 475-484.
  • Krishna Kant Tiwari ,Susheel Tiwari , Sriram Yadav " Analyze the Different Kernel Function in SVM for IDS" International Journal of Advanced Research in Computer Science and Electronics Engineering, Vol-2, 2013. Pp 623-632.
  • SannasiGanapathy, KanagasabaiKulothungan,"Intelligent feature selection and classification techniques for intrusion detection in networks: a survey" EURASIP Journal on Wireless Communications and Networking , Springer 2013. Pp 1-16.
  • Gang Wang, Jinxing Hao, Jian Ma, Lihua Huang "A new approach to intrusion detection using Artificial Neural Networks and fuzzy clustering" Expert Systems with Applications, Elsevier 2010. Pp 1-8.
  • RituRanjani Singh, Neetesh Gupta "To Reduce the False Alarm in Intrusion DetectionSystem using self Organizing Map" International journal of Computer Science and its Applications, 2010. Pp 65-72.
  • AnshulChaturvedi and Prof. VineetRichharia "A Novel Method for Intrusion Detection Based on SARSA and Radial Bias Feed Forward Network (RBFFN)" in international journal of computers & technology vol 7, no 3.
  • Mohammad Behdad, Luigi Barone, Mohammed Bennamoun and Tim French "Nature-Inspired Techniques in the Context of Fraud Detection" in ieee transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 42, no. 6, november 2012.
  • Alberto Fernandez, Maria Jose del Jesus and Francisco Herrera "On the influence of an adaptive inference system in fuzzy rule based classification system for imbalanced data-sets" in Elsevier Ltd. All rights reserved 2009.
  • P. Garcia-Teodoro, J. Diaz-Verdejo, G. Macia-Fernandez and E. Vazquez "Anomaly-based network intrusion detection: Techniques, Systems and challenges" in Elsevier Ltd. All rights reserved 2008.
  • Terrence P. Fries "A Fuzzy-Genetic Approach to Network Intrusion Detection" in GECCO 08, July12–16, 2008, Atlanta, Georgia, USA.
  • Shailendra Singh, Sanjay Silakari "An Ensemble Approach for Cyber Attack Detection System: A Generic Framework" 14th ACIS, IEEE 2013. Pp 79-85
  • X. Li et al. , "Smart Community: An Internet of Things Application," IEEE Commun. Mag. , vol. 49, no. 11, 2011, pp. 68–75.
  • Jain and Upendra "An Efficient intrusion detection based onDecision Tree Classifier using feature Reduction", InternationalJournal of scientific and research Publications , Vol. 2, Jan. 2012.
  • Muda, Y. Yassin, M. N. Sulaiman and N. I. Udzir, " A K-Means andNaive Bayes Learning Aproach for Better Information Detection",Information Technology journal, Asian Network For scientificInformation publisher, Vol. 10 , 2011.
  • Zhang and M. Zulkernine, "Network Intrusion Detection usingRandom Forests", School of Computing Queen's University,Kingston Ontario, 2006.
  • . Sunita Patel and JyotiSondhi "A Review of Intrusion Detection Technique using Various Technique of Machine Learning and Feature Optimization Technique" in International Journal of Computer Applications, Volume 93 – No 14, May 2014