CFP last date
22 April 2024
Call for Paper
May Edition
IJCA solicits high quality original research papers for the upcoming May edition of the journal. The last date of research paper submission is 22 April 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS)

by Sherif M. Badr
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 61 - Number 4
Year of Publication: 2013
Authors: Sherif M. Badr
10.5120/9918-4526

Sherif M. Badr . Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS). International Journal of Computer Applications. 61, 4 ( January 2013), 41-49. DOI=10.5120/9918-4526

@article{ 10.5120/9918-4526,
author = { Sherif M. Badr },
title = { Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS) },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 61 },
number = { 4 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 41-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume61/number4/9918-4526/ },
doi = { 10.5120/9918-4526 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:08:13.148474+05:30
%A Sherif M. Badr
%T Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 61
%N 4
%P 41-49
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Intrusion Detection System (IDS) has increasingly become a crucial issue for computer and network systems. Optimizing performance of IDS becomes an important open problem which receives more and more attention from the research community. This paper, design and develop a proposed multi-layer intrusion detection model to achieve high efficiency and improve the detection and classification rate accuracy. Also the proposed model was improved the detection rate for known and unknown attacks by training the hybrid model on the known intrusion data. Then the model applied for unknown attacks by introducing new types of attacks that are never seen by the training module. The experimental results showed that the proposed multi-layer model using C5 decision tree achieves higher classification rate accuracy, and less false alarm rate

References
  1. Sherif M. Badr, "Security Architecture for Internet Protocols", PhD thesis, Military Technical Collage, 2001.
  2. Asmaa Shaker Ashoor, Prof. SharadGore,"Importance of Intrusion Detection System (IDS)", International Journal of Scientific & Engineering Research (IJSER), Volume 2, Issue 1, January-2011.
  3. Naelahokasha, Sherif M. Badr, Abd El Fatah Hegazy, "Towards Ontology-Based Adaptive Multilevel Model for Intrusion Detection and Prevention System (AMIDPS)", Egyptian science journal (ESC), Vol. 34, No. 5, September 2010.
  4. Z. S. Pan, S. C. Chen, G. B Hu and D. Q. Zhang, "Hybrid Neural Network and C4. 5 for Misuse Detection, " In Machine Learning and Cybernetics, pp. 2463-2467. Xi'an, 2003.
  5. SrinivasMukkamala, "Intrusion detection using neural networks and support vector machine", Proceedings of the IEEE International Honolulu, 2002.
  6. M. Moradi, and M. Zulkernine, "A Neural Network Based System for Intrusion Detection and Classification of Attacks,"IEEE International Conference on Advances in Intelligent Systems – Theory and Applications, November 15-18, 2004.
  7. S. Peddabachigari, A. Abraham, C. Grosan and J. Thomas, "Modeling intrusion detection system using hybrid intelligent systems," J. Network Comput. Appl. , 30: pp 114-132, 2007.
  8. OzgurDepren, Murat Topallar, EminAnarim and M. Kemal Ciliz, "An intelligent intrusion detection system for anomaly and misuse detection in computer networks," Expert Systems with Applications, Volume 29, Issue 4, pp 713-722, 2005.
  9. Dewan Md. Farid, NouriaHarbi, EmnaBahri, el "Attacks Classification in Adaptive Intrusion Detection using Decision Tree" International Conference on Computer Science (ICCS 2010), 29-31 March, 2010, Rio De Janeiro, Brazil.
  10. Mohammad SazzadulHoque, Md. Abdul Mukit and Md. Abu NaserBikas," An Implementation of Intrusion Detection System using Genetic Algorithm ", International Journal of Network Security & Its Applications (IJNSA), Vol. 4, No. 2, March 2012.
  11. M. R. Sabhnani and G. Serpen, "Application of Machine Learning Algorithms to KDD Intrusion Detection Dataset within Misuse Detection Context" Proceedings of International Conference on Machine Learning: Models, Technologies, and Applications, Las Vegas, Nevada, 2003, pp. 209-215.
  12. KDD Cup 1999. http://kdd. ics. uci. edu/databases/kddcup 99/kddcup99. html, October 2007.
  13. M. Tavallaee, E. Bagheri, W. Lu, and A. Ghorbani, "A Detailed Analysis of the KDD CUP 99 Data Set," Submitted to Second IEEE Symposium on Computational Intelligence for Security and Defense Applications (CISDA), 2009.
  14. Bobor, V. "Efficient Intrusion Detection System Architecture Based on Neural Networks and Genetic Algorithms", Department of Computer and Systems Sciences, Stockholm University / Royal Institute of Technology, KTH/DSV, 2006.
  15. Aickelin, U. , J. Greensmith, and J. Twycross. "Immune System Approaches to Intrusion Detection -A Review ", Natural Computing, Springer, 2007.
  16. Yao, J. T. , S. L. Zhao, and L. V. Saxton, . A Study on Fuzzy ID. In Proceedings of the DM, ID, and Data Networks Security, SPIE, Vol. 5812, pp. 23-30, Orlando, Florida, USA, 2005.
  17. Gang, W. , J. Hao, J. Ma and L. Huang, "A new approach to intrusion detection using artificial neural networks and fuzzy clustering," Expert Syst. Appl. , 37: 6225-6232, 2010.
  18. L Prema RAJESWARI and Kannan ARPUTHARAJ, "An Active Rule Approach for Network Intrusion Detection with Enhanced C4. 5 Algorithm," International Journal of Communications, Network and Systems Sciences (IJCNS), 2008, 4, 285-385.
  19. "NSL-KDD data set for network-based intrusion detection systems," Available on: http://nsl. cs. unb. ca/NSL-KDD/, March 2009.
  20. Y. Bouzida, F. Cuppens, "Neural networks vs. decision trees for intrusion detection," IEEE/IST Workshop on Monitoring, Attack Detection and Mitigation, Germany, 28-29 September 2006.
  21. SaharSelimFouad "Implementation of Intelligent Techniques for Intrusion Detection Systems", master thesis, Egypt, 2011.
Index Terms

Computer Science
Information Sciences

Keywords

intrusion detection Decision Tree network security

Powered by PhDFocusTM