Call for Paper - March 2023 Edition
IJCA solicits original research papers for the March 2023 Edition. Last date of manuscript submission is February 20, 2023. Read More

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

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 61 - Number 4
Year of Publication: 2013
Sherif M. Badr

Sherif M Badr. Article: Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS). International Journal of Computer Applications 61(4):41-49, January 2013. Full text available. BibTeX

	author = {Sherif M. Badr},
	title = {Article: Implementation of Intelligent Multi-Layer Intrusion Detection Systems (IMLIDS)},
	journal = {International Journal of Computer Applications},
	year = {2013},
	volume = {61},
	number = {4},
	pages = {41-49},
	month = {January},
	note = {Full text available}


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


  • Sherif M. Badr, "Security Architecture for Internet Protocols", PhD thesis, Military Technical Collage, 2001.
  • Asmaa Shaker Ashoor, Prof. SharadGore,"Importance of Intrusion Detection System (IDS)", International Journal of Scientific & Engineering Research (IJSER), Volume 2, Issue 1, January-2011.
  • 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.
  • 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.
  • SrinivasMukkamala, "Intrusion detection using neural networks and support vector machine", Proceedings of the IEEE International Honolulu, 2002.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • KDD Cup 1999. http://kdd. ics. uci. edu/databases/kddcup 99/kddcup99. html, October 2007.
  • 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.
  • 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.
  • Aickelin, U. , J. Greensmith, and J. Twycross. "Immune System Approaches to Intrusion Detection -A Review ", Natural Computing, Springer, 2007.
  • 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.
  • 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.
  • 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.
  • "NSL-KDD data set for network-based intrusion detection systems," Available on: http://nsl. cs. unb. ca/NSL-KDD/, March 2009.
  • 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.
  • SaharSelimFouad "Implementation of Intelligent Techniques for Intrusion Detection Systems", master thesis, Egypt, 2011.