CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System

by S.j.sathish Aaron Joseph, R.balasubramanian
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 113 - Number 15
Year of Publication: 2015
Authors: S.j.sathish Aaron Joseph, R.balasubramanian
10.5120/19904-2015

S.j.sathish Aaron Joseph, R.balasubramanian . A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System. International Journal of Computer Applications. 113, 15 ( March 2015), 33-40. DOI=10.5120/19904-2015

@article{ 10.5120/19904-2015,
author = { S.j.sathish Aaron Joseph, R.balasubramanian },
title = { A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System },
journal = { International Journal of Computer Applications },
issue_date = { March 2015 },
volume = { 113 },
number = { 15 },
month = { March },
year = { 2015 },
issn = { 0975-8887 },
pages = { 33-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume113/number15/19904-2015/ },
doi = { 10.5120/19904-2015 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:51:02.328693+05:30
%A S.j.sathish Aaron Joseph
%A R.balasubramanian
%T A Comprehensive Survey of Technologies for Building a Hybrid High Performance Intrusion Detection System
%J International Journal of Computer Applications
%@ 0975-8887
%V 113
%N 15
%P 33-40
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion detection plays a vital role in maintaining the stability of any network. The major requirements for any intrusion detection system are speed, accuracy and less memory. Though various intrusion detection methods are available, they excel at some points while lack in the others. This paper presents a comprehensive survey of the technologies that are used for detecting intrusions. It analyzes the pros and cons of each technology and the literature works that utilizes these technologies. Challenges faced by the current IDS and the requirements for IDS in the current network scenario are discussed in detail. A detailed study on the datasets that can be used for effective building of an IDS is discussed. The research framework is proposed and a discussion of the various technologies that can be used for improving the efficiency of the IDS is provided.

References
  1. S. Sonawane, Sh. Pardeshi, G. Pradad, A Survey on Intrusion Detection Techniques (Department of Information Technology, Technocrats Institute of Technology, Bhopal, India, April 2012.
  2. N. Jacob, C. Brodley, Offloading IDS Computation to the GPU (Computer Science Department, Tufts University,Medford, 2006.
  3. Vokorokos, Liberios, Michal Ennert, and JánRadušovský. "A survey of parallel intrusion detection on graphical processors. " Central European Journal of Computer Science 4. 4 (2014): 222-230.
  4. M. S. Clos, A Framework for Network Traffic Analysis Using GPUs (UniversitatPolitecnica de Catalunya, Barcelona, 2010).
  5. Elkan C. Results of the KDD'99 classifier learning contest. SIGKDD. Explor. Newsl 1999;1(2):63e4.
  6. Chen ZF, Qian PD, Chen ZF. Application of PSO-RBF neural network in network intrusion detection. In: Proceedings of the 3rd International Symposium on Intelligent Information Technology Application 2009. p. 362e364.
  7. RG Reynolds. Flocks, herds, and schools: a distributed behavioral model. Computer Graphics 1987; 21(4):25e34.
  8. Ma R, Liu Y, Lin X, Wang Z. Network anomaly detection using RBF neural network with hybrid QPSO. In: Proceedings of the IEEE International Conference on Networking, Sensing and Control 2008b. p. 1284e1287.
  9. Ma R, Liu Y, Lin X. Hybrid QPSO based wavelet neural networks for network anomaly detection. In: Proceedings of the Second Workshop on Digital Media and its Application in Museum and Heritages. 2007. p. 442e447.
  10. Liu H, Jian Y, Liu S. New intelligent intrusion detection methods based on attribute reduction and parameters optimization of SVM. In: Proceedings of the Second International Workshop on Education Technology and Computer Science (ETCS). 2010. P. 202e205.
  11. Zhou T, Li Y, Li J. Research on intrusion detection of SVM based on PSO. In: Proceedings of the International Conference on Machine Learning and Cybernetics 2009. p. 1205e1209.
  12. Wang J, Hong X, Ren R, Li T. A real-time intrusion detection system based on PSO-SVM. In: Proceedings of the International Workshop on Information Security and Application 2009 (IWISA 2009). p. 319e321.
  13. Zhao C, Wang W. An improved PSO-Based rule extraction algorithm for intrusion detection. In: Proceedings of International Conference on the Computational Intelligence and Natural Computing 2009 (CINC '09). p. 56e58.
  14. Alipour H, Khosrowshahi E, Esmaeili M, Nourhossein M. ACOFCR: applying ACO-based algorithms to induct FCR. In: Proceedings of the World Congress on Engineering (IWCE) 008. p. 12e17.
  15. Abadeh MS, Habibi J. A hybridization of evolutionary fuzzy systems and ant colony optimization for intrusion detection. The ISC International Journal of Information Security 2010; 2(1):33e46.
  16. Ramos V, Abraham A, ANTIDS: Self organized ant based clustering model for intrusion detection system. In: Proceedings of The Fourth IEEE International Workshop on Soft Computing as Trans disciplinary Science and Technology (WSTST'05) 2005. p. 977e986.
  17. Tsang W, Kwong S. Unsupervised anomaly intrusion detection using ant colony clustering model. In: Proceedings of the 4th IEEE International Workshop on Soft Computing as Trans disciplinary Science and Technology 2005. p. 223e232.
  18. Tsang CH, Kwong S. Multi-agent intrusion detection system in industrial network using ant colony clustering approach and unsupervised feature extraction. In: Proceedings of the IEEE International Conference on Industrial Technology 2005 (ICIT 2005). p. 51e56.
  19. Feng Y, Zhong J, Ye CY, Wu ZF. Clustering based on self organizing ant colony networks with application to intrusion detection. In: Proceedings of the Sixth International Conference on Intelligent Systems Design and Applications (ISDA '06). 2006. P. 1077e1080.
  20. Y. Zhang, W. Lee, Intrusion detection in wireless ad-hoc networks, in: The 6th Annual International Conference on Mobile Computing and Networking, Boston, MA, USA, 2000, pp. 275–283.
  21. D. P. Jeyepalan, E. Kirubakaran ,(April 2013),"A Novel Graph Based Clustering Approach for Network Intrusion Detection", International Journal of Computational Intelligence and Information Security, Vol. 4 No. 4,ISSN: 1837-7823.
  22. D. P. JeyepalanE. Kirubakaran,(2014), "A Co-operative Game Theoretic Approach to Improve the Intrusion Detection System in a Network using Ant Colony Clustering", International Journal of Computer Applications,Volume 87 - Number 14.
  23. Marinakis, Yannis, et al. , (2011), "A hybrid ACO-GRASP algorithm for clustering analysis. " Annals of Operations Research 188. 1: 343-358.
  24. Ganapathy, Sannasi, et al. , (2013), "Intelligent feature selection and classification techniques for intrusion detection in networks: a survey. " EURASIP Journal on Wireless Communications and Networking 2013. 1: 1-16.
  25. Aziz, Amira Sayed A. , and Aboul Ella Hassanien. "Multilayer Machine Learning-Based Intrusion Detection System. " Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations. Springer Berlin Heidelberg, 2014. 225-247.
  26. Luo, Bin, and Jingbo Xia. "A novel intrusion detection system based on feature generation with visualization strategy. " Expert Systems with Applications 41. 9 (2014): 4139-4147.
  27. Zhang, Ji, et al. "Detecting anomalies from big network traffic data using an adaptive detection approach. " Information Sciences (2014).
  28. Chen, Tieming, et al. "Efficient classification using parallel and scalable compressed model and its application on intrusion detection. " Expert Systems with Applications 41. 13 (2014): 5972-5983.
  29. Vasudevan, ARi, E. Harshini, and S. Selvakumar. "SSENet-2011: a network intrusion detection system dataset and its comparison with KDD CUP 99 dataset. " Internet (AH-ICI), 2011 Second Asian Himalayas International Conference on. IEEE, 2011.
  30. P. Fanfara, A. Pekár, Usage of Hybrid Honeypots an Intrusion Detection System Mechanism, SCYR 2012: Proceedings from conference : 12th Scientific Conference of Young Researchers, 2012
  31. V. Marinova-Boncheva, A Short Survey of Intrusion Detection Systems (Institute of Information Technologies, Sofia, 2007)
  32. Kolias, Constantinos, Georgios Kambourakis, and M. Maragoudakis. "Swarm intelligence in intrusion detection: A survey. " computers & security 30. 8 (2011): 625-642.
Index Terms

Computer Science
Information Sciences

Keywords

Intrusion detection system KDD CUP 99 SSENet Evolutionary algorithms Graph Database Big Data

Powered by PhDFocusTM