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Network Intrusion Detection Systems based Neural Network: A Comparative Study

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
Berlin H. Lekagning Djionang, Gilbert Tindo

Berlin Lekagning H Djionang and Gilbert Tindo. Network Intrusion Detection Systems based Neural Network: A Comparative Study. International Journal of Computer Applications 157(5):42-47, January 2017. BibTeX

	author = {Berlin H. Lekagning Djionang and Gilbert Tindo},
	title = {Network Intrusion Detection Systems based Neural Network: A Comparative Study},
	journal = {International Journal of Computer Applications},
	issue_date = {January 2017},
	volume = {157},
	number = {5},
	month = {Jan},
	year = {2017},
	issn = {0975-8887},
	pages = {42-47},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2017912717},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Neural networks are artificial learning systems. For more than two decades, they have help for detecting hostile behaviors in a computer system. This review describes those systems and theirs limits. It defines and gives neural networks characteristics. It also itemizes neural networks which are used in intrusion detection systems. The state of the art on IDS made from neural networks is reviewed. In this paper, we also make a taxonomy and a comparison of neural networks intrusion detection systems. We end this review with a set of remarks and future works that can be done in order to improve the systems that have been presented. This work is the result of a meticulous scan of the literature.


  1. J.P. A NDERSON. “Computer Security Threat Monitoring and Surveillance”. Rapport Technique, James P. Anderson Company, Fort Washington, Pennsylvania, April 1980.
  2. Ludovic Me, « Méthodes et outils de la détection d’intrusions », Supelec.
  3. Guillaume Hiet, « Détection d’instructions paramétrée par la politique de sécurité grâce au contrôle collaboratif des flux d’informations au sein du système d’exploitation et des applications: mise en œuvre sous linux pour les programmes java » Université de Rennes, Decembre 2008
  4. Asmaa Shaker, Sharer Gore « Importance of Intrusion Detection System » International Journal of Scientific & Engineering Research Janvier 2011.
  5. Nicoleta Minoiu « comparaison entre l’analyse logic et probit et les réseaux de neurones »
  6. G. DREYFUS “les réseaux de neurones” Mécanique Industriel et Matériaux, n51, septembre 1998
  7. Vladimir Golovko, Pavel Kochurko “Intruision recognition using neural networks” International Scientific Journal of computing, 2005, vol. 4, Issue3, 37-42
  8. Mahbod Tavallaee && all “A Detailed Analysis of the KDD CUP 99 Data Set” Proceeding of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Application (CISDA 2009)
  9. H. Debar && all, “A neural network component for an intrusion detection system”, in IEEE Symposium on Research in Computer Security and Privacy, Oakland, 4– 6 May 1992 (IEEE, Amsterdam, 1992), pp. 240– 250
  10. James Canady “Artificial Neural Networks forMisuse Detection,” Proceedings, National Information Systems Security Conference (NISSC), 98
  11. ALAN BIVENS && all “Network based intrusion detection using neural network” Intelligent Engineering Systems through Artificial Neural network ANNIE-2002, St. Louis,MO,vol. 12, ASME Press, New York,NY, 2002, pp. 579-584
  12. Mehdi MORADI and Mohammad ZULKERNINE, “A Neural Network based System for intrusion detection and Classification of Attacks” In 2004 IEEE International on Advances in Intelligent Systems.
  13. Leanid VAITSEKHOVICH, Vladimir GOLOVKO « Employment of neural network baser classifier for intrusion detection » acta mechanica et automatica, vol2, no4, 2008
  14. Aslihan Ozkaya && Bekir Karlik “Protocole Type Based Intrusion Detection Using RBF Neural Network” International Journal of Artificial Intelligence and Expert Systems (IJAE), volume (3): Issue (4):2012
  15. Muna Mhammad && Monica Mehrotra “Design Network Intrusion Detection System using Hybrid Fuzzy-Neural Network” International Journal of Computer Science and Security, volume (4): Issue (3): 2012
  16. Yousef Abuadlla && all “Flow-Based Anomaly Intrusion Detection System Using Two Neural Network Stage”, Computer Science and Information systems 11(2): 601-622: 2012
  17. Quamar && all “A Deep Learning Approach for Network Intrusion Detection System” BICT 15 Proceeding of 9th EAI International Conférence on Bio-inspired Information and Communications Technologies (BIONETICS) pages 21-26, December 2015
  18. Srinivas Mukkamala && all “Intrusion detection using an ensemble of intelligent paradigms”, Journal Network and Computer Applications 28 (2005), 167-182
  19. Iftikhar Ahmad && all “Performance Comparison between Backpropagation Algorithms Apllied to Intrusion Detection in Computer Network Systems” WSEAS International Conference on NEURAL NETWORKS, Sofia, Bulgaria, May 2-4, 2008
  20. Iftikhar Ahmad && all “Application of Artificial Neural Networmanyk in Detection of Probing Attacks” 2009 IEEE Symposium on Industrial Electronics and Applications(ISIEA 2009), October 4-6, 2009, Kuala Lumpur, Malaysia
  21. Khattab Ali && all “The Effect of Fuzzification on neural Networks Intrusion Detection system” IEEE computer society 2009
  22. Mohammad Khubeb Siddiqui and Shams Naahid “Analysis of KDD CUP 99 Dataset using Clustering based Data Mining”, International Journal of Database


Intrusion detecting system, NIDS, neural network, MLP