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A Review of Soft Computing Solutions to Intrusion in Computer Network

by Gargee Shukla, Anamika Shukla Sharma, Hari Shankar Hota
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 149 - Number 12
Year of Publication: 2016
Authors: Gargee Shukla, Anamika Shukla Sharma, Hari Shankar Hota
10.5120/ijca2016911644

Gargee Shukla, Anamika Shukla Sharma, Hari Shankar Hota . A Review of Soft Computing Solutions to Intrusion in Computer Network. International Journal of Computer Applications. 149, 12 ( Sep 2016), 9-14. DOI=10.5120/ijca2016911644

@article{ 10.5120/ijca2016911644,
author = { Gargee Shukla, Anamika Shukla Sharma, Hari Shankar Hota },
title = { A Review of Soft Computing Solutions to Intrusion in Computer Network },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2016 },
volume = { 149 },
number = { 12 },
month = { Sep },
year = { 2016 },
issn = { 0975-8887 },
pages = { 9-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume149/number12/26048-2016911644/ },
doi = { 10.5120/ijca2016911644 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:54:33.558122+05:30
%A Gargee Shukla
%A Anamika Shukla Sharma
%A Hari Shankar Hota
%T A Review of Soft Computing Solutions to Intrusion in Computer Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 149
%N 12
%P 9-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the wide adoption of internet, there lies threat to sensitive information being shared on the network. An intrusion can be defined as an act or a number of acts in a sequence that either cause a compromise or intend to compromise the information. These intrusions need somehow to be detected so that the harm caused by them may be prevented. A system that keeps looking for activities in a computer or a network to detect intrusions is called an Intrusion Detection System (IDS). Many novel methods of intrusion detection involve the use of Soft Computing tools. Soft Computing (SC) is a collection of methods used for developing intelligent systems for the problems for which conventional techniques have not given low cost or complete solutions. Artificial Neural Networks (ANN), Fuzzy Logic theory (FL) and Genetic Algorithm (GA) represent the most common tools of soft computing. The capability of soft computing tools to tolerate imprecision, partial truth, uncertainty and ability to provide low solution costs to real world problems and computationally intelligent problems are the obvious reasons which has made this approach widely accepted in field of Intrusion Detection In this research work, we have collected about thirty papers to study how different soft computing tools and techniques can be utilized to develop efficient and robust Intrusion detection Systems.

References
  1. Zadeh Lofty A. 1994, Soft computing and fuzzy logic, IEEE Software, Volume 11 Issue 6, 46-58.
  2. Denning, D.E. 1987, IEEE Transactions on Software Engineering , Vol. Se-13, No. 2, February 1987, pp 222-232.
  3. Cannady, J. 1998, Applying Neural Networks to Misuse Detection. In Proceedings of the 21st National Information Systems Security Conference.
  4. Sebring, M., Shellhouse, E., Hanna, M. & Whitehurst, R. 1988, Expert Systems in Intrusion Detection: A Case Study, Proceedings of the 11th National Computer Security Conference 1988.
  5. Guan J., Liu D.-X, Wang T 2004, Applications of fuzzy data mining methods for intrusion detection systems, Computational Science and Its Applications - ICCSA 2004: International conference assisi Italy may 2004 proceedings part III.
  6. Examining Different Types of Intrusion Detection Systems 2014. Retrieved from http://www.dummies.com/how-to/content/examining-different-types-of-intrusion-detection-s.html
  7. Feng W, Zhang Q, Hu G., Huang J.X. 2014, Mining network data for intrusion detection through combining SVMs with ant colony networks, Future Generation Computer Systems ,37 ,2014, 127–140.
  8. Song J., Takakura H., Okabe Y., Kwon Y.2009, Unsupervised Anomaly Detection Based on Clustering and Multiple One-class SVM, IEICE Transactions on Communications E92-B (06) ,2009, 1981–1990.
  9. Hoz, D. l., Hoz, D. L., Ortiz, A., Ortega, J., & Prieto, B. (n.d.) 2015, PCA filtering and probabilistic SOM for network intrusion detection , Neurocomputing, , 164( 21), September 2015, pp 71–81
  10. Ibrahim, L. M., Basheer, D. T., & Mahmod, S. (n.d.) 2013. A Comparison Study For Intrusion Database (Kdd99, Nsl-Kdd) Based On Self Organization Map (Som) Artificial Neural Network Journal of Engineering Science and Technology,8(1),107 - 119
  11. Sondhiya R., Shreevastav M., Mishra M. 2013,To Improve Security in Cloud Computing with Intrusion detection system using Neural Network International Journal of Soft Computing and Engineering (IJSCE), 3(2), May 2013.
  12. Devaraju S., Ramakrishnan S. 2013 , Detection of Accuracy For Intrusion Detection System Using Neural Network Classifier, International Journal of Emerging Technology and Advanced Engineering, 3(1), January 2013.
  13. Devaraju S., Ramakrishnan S. 2014,Performance Comparison For Intrusion Detection System Using Neural Network With Kdd Dataset ,ICTACT Journal On Soft Computing, April 2014, 04(03).
  14. Gowrison G., Ramar K, Muneeswaran K., Revathi T.2013, Minimal complexity attack classification intrusion detection system, Applied Soft Computing, 13, 2013, 921–927.
  15. Veerwal D., Choudhary N. & Singh D., Ensemble of Soft Computing Techniques for Intrusion Detection 2013. Global Journal of Computer Science and Technology Network, Web & Security, 13(13),2013
  16. Amini, M., Rezaeenoor, J., & Hadav, E. (n.d.).2014, Effective Intrusion Detection with a Neural Network Ensemble Using Fuzzy Clustering and Stacking Combination Method ,Journal of Computing and Security, Volume 1, Number 4 ,293-305 ,October 2014.
  17. Belacel N. 2000,Multicriteria assignment method PROAFTN: Methodology and medical application, European Journal of Operational Research 2000; 125 (1) ,175 – 183.
  18. Al-Obeidat F., Belacel, N., Carretero, J.A., Mahanti, P. 2011, An evolutionary framework using particle swarm optimization for classification method PROAFTN., Applied Soft Computing 2011;11(8):4971 – 4980.
  19. El-Alfy, E.-S. M., & Al-Obeidat, F. N. (n.d.) 2014. A multicriterion fuzzy classification method with greedy attribute selection for anomaly-based intrusion detection ,Procedia Computer Science 34 ,2014, 55 – 62.
  20. Balan, E. V., Priyan, M. K., C., G., & Devi, G. U. (n.d.).2015, Fuzzy Based Intrusion Detection Systems in MANET , Procedia Computer Science,2nd International Symposium on Big Data and Cloud Computing. ,50 ,2015, 109 – 114
  21. Dickerson, J. E. and Dickerson J. A. 2000 ,Fuzzy network profiling for intrusion detection. In Proc. of NAFIPS 19th International Confer-ence of the North American Fuzzy Information Processing Society At-lanta ,301–306.
  22. Shamshirband, S., Anuar, N. B., Kiah, L. M., & Misra, S. (n.d.). 2014, Anomaly Detection using Fuzzy Q-learning Algorithm , Acta Polytechnica Hungarica Vol. 11( 8), 2014.
  23. Abraham, A., Jain, R., Sanyal, S., & Han, S. Y. (n.d.)., SCIDS: A Soft Computing Intrusion Detection System, Chapter Distributed Computing - IWDC 2004,Volume 3326 of the series Lecture Notes in Computer Science, 252-257
  24. Ahmad I., Hussain M. ,Alghamdi A., Alelaiwi A. 2014, Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components, Neural Computing & Applications, 24(7), 1671-1682.
  25. Kuang F, Xua W, Zhang S , 2014, A novel hybrid KPCA and SVM with GA model for intrusion detection , Applied Soft Computing, 18, 178–184.
  26. Murthy, Y. V., Harish, K., Varma, D. K., Sriram, K., & Revanth, B. V. (n.d.) 2014.Hybrid Intelligent Intrusion Detection System using Bayesian and Genetic Algorithm (BAGA): Comparitive Study International Journal of Computer Applications ,99 (2), August 2014 .
  27. Bridges Susan M., Vaughn Rayford B.2000, Fuzzy Data Mining And Genetic Algorithms Applied To Intrusion Detection Presented at the National Information Systems Security Conference (NISSC), October 16-19, 2000, Baltimore, MD.
  28. Wang Wengdong , Susan M. Bridges 2000,Genetic Algorithm Optimization of Membership Functions for Mining Fuzzy Association Rules , Presented at the International Joint Conference on Information Systems, Fuzzy Theory and Technology Conference, Atlantic City, N.J. March 2, 2000.
  29. Elhag S. , Fernandez A., Bawakid A. , Alshomrani S. , Herrera F. 2015, On the combination of genetic fuzzy systems and pair wise learning for improving detection rates on Intrusion Detection Systems Expert Systems with Applications, 42 ,193–202,2015.
  30. P. Arun Raj Kumar, Selvakumar 2013, Detection of distributed denial of service attacks using an ensemble of adaptive and hybrid neuro-fuzzy systems, Computer Communications , 36(3),2013, 303–319.
Index Terms

Computer Science
Information Sciences

Keywords

Soft Computing Fuzzy Logic Genetic Algorithm Intrusion Detection System (IDS)

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