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Design Network Intrusion Detection System using Hybrid Fuzzy-Neural Network
muna mhammad taher jawhar, Monica Mehrotra
Pages - 285 - 294     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 4   Issue - 3    |    Publication Date - July 2010  Table of Contents
intrusion detection, neural network, fuzzy claustering
As networks grow both in importance and size, there is an increasing need for effective security monitors such as Network Intrusion Detection System to prevent such illicit accesses. Intrusion Detection Systems technology is an effective approach in dealing with the problems of network security. In this paper, we present an intrusion detection model based on hybrid fuzzy logic and neural network. The key idea is to take advantage of different classification abilities of fuzzy logic and neural network for intrusion detection system. The new model has ability to recognize an attack, to differentiate one attack from another i.e. classifying attack, and the most important, to detect new attacks with high detection rate and low false negative. Training and testing data were obtained from the Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set
CITED BY (28)  
1 Mansouri, m., golsefid, m. t., & nematbakhsh, n. (2015). a hybrid intrusion detection system based on multilayer artificial neural network and intelligent feature selection. cumhuriyet science journal, 36(3), 2686-2692.
2 Hussain, J., Lalmuanawma, S., & Chhakchhuak, L. (2015). A Novel Network Intrusion Detection System Using Two-Stage Hybrid Classification Technique. IJCCER, 3(2), 16-27.
3 Abuadlla, Y., Kvascev, G., Gajin, S., & Jovanovic, Z. (2014). Flow-based anomaly intrusion detection system using two neural network stages. Computer Science and Information Systems, 11(2), 601-622.
4 Hameed, S. M., & Rashid, O. F. Intrusion Detection Approach Based on DNA Signature.
5 Choi, Y. B., Sershon, C., Briggs, J., & Clukey, C. Survey of Layered Defense, Defense in Depth and Testing of Network Security.
6 ONUwA, O. B. (2014). Improving Network Attack Alarm System: A Proposed Hybrid Intrusion Detection System Model.
7 Mgabile, T. (2014). Network intrusion detection system using neural networks approach in networked biometrics system (Doctoral dissertation).
8 Chan, G. Y., Lee, C. S., & Heng, S. H. (2014). Defending against XML-related attacks in e-commerce applications with predictive fuzzy associative rules. Applied Soft Computing, 24, 142-157.
9 Kachurka, P., & Golovko, V. (2014). fusion of recirculation neural networks for real-time network intrusion detection and recognition. international Journal of Computing, 11(4), 383-390.
10 Sethuramalingam, S., & Naganathan, E. R. A Fuzzy Model for Network Intrusion Detection.
11 Chan, G. Y., Lee, C. S., & Heng, S. H. (2013). Discovering fuzzy association rule patterns and increasing sensitivity analysis of XML-related attacks. Journal of Network and Computer Applications, 36(2), 829-842.
12 Chandrashekhar, A. M., & Raghuveer, K. (2013). Fortification of hybrid intrusion detection system using variants of neural networks and support vector machines. International Journal of Network Security & Its Applications, 5(1), 71-90.
13 Husagic-Selman, A., Koker, R., & Selman, S. (2013, October). Intrusion detection using neural network committee machine. In Information, Communication and Automation Technologies (ICAT), 2013 XXIV International Symposium on (pp. 1-6). IEEE.
14 Parate, S., Nirkhi, S. M., & Dharaskar, R. V. (2013, December). Application of Network Forensics for Detection of Web Attack using Neural Network. In IJCA Proceedings on National Conference on Innovative Paradigms in Engineering & Technology 2013 (No. 2, pp. 28-31). Foundation of Computer Science (FCS).
15 Chana, G. Y., Leea, C. S., & Hengb, S. H. (2013). peanfis-farm framework in defending against web service attacks.
16 Hameed, S. M., Saad, S., & AlAni, M. F. (2013). An Extended Modified Fuzzy Possibilistic C-Means Clustering Algorithm for Intrusion Detection. Lecture Notes on Software Engineering, 1(3), 273-278.
17 Selman, A. H. (2013). Intrusion Detection System using Fuzzy Logic.
18 Chan, G. Y., Lee, C. S., & Heng, S. H. (2012). Policy-enhanced ANFIS model to counter SOAP-related attacks. Knowledge-Based Systems, 35, 64-76.
19 Naoum, R. S., & Al-Sultani, Z. N. (2012). Learning Vector Quantization (LVQ) and k-Nearest Neighbor for Intrusion Classification. World of Computer Science and Information Technology Journal (WCSIT), 2(3), 105-109.
20 Choi, B. H., Choi, S. K., & Cho, K. S. (2012). Anomaly Detection Scheme of Web-based attacks by applying HMM to HTTP Outbound Traffic.
21 Hameed, S. M., & Sulaiman, S. S. (2012). Intrusion Detection Using a Mixed Features Fuzzy Clustering Algorithm. Iraq Journal of Science (IJS), 53(2), 427-434.
22 Chandrashekar, A. M., & Raghuveer, K. (2012, October). Fusion of multiple data mining techniques for effective network intrusion detection: a contemporary approach. In Proceedings of the Fifth International Conference on Security of Information and Networks (pp. 178-182). ACM.
23 Dharmar, V., Parveen, M. A. R., & Bhuvaneswaran, R. S. Black Hole Detection in Adhoc Networks using Neural Networks. Dear Scholars, 29.
24 Al-Sultani, Z. N. M. A. (2012). an enhanced resilient backpropagation artificial neural network for intrusion detection system (doctoral dissertation, middle east university).
25 Kachurka, P., & Golovko, V. (2011, September). Neural network approach to real-time network intrusion detection and recognition. In Intelligent Data Acquisition and Advanced Computing Systems (IDAACS), 2011 IEEE 6th International Conference on (Vol. 1, pp. 393-397). IEEE.
26 Alebachew, K. (2011). Designing a Hybrid Classifier for Network Intrusion Detection System (Doctoral dissertation, AAU).
27 Kukielka, R., & KOTuLSKI, Z. (2011). Systemy wykrywania intruzów wykorzystujace metody sztucznej inteligencji. Przeglad Telekomunikacyjny+ Wiadomosci Telekomunikacyjne, (4), 114-121.
28 R. M. Patil, M. R. Patil, Dr. K. V. Ramakrishnan and Dr. T.C.Manjunath. “IDDP: Novel Development of an Intrusion Detection System through Design Patterns”. International Journal of Computer Applications, 7(12), pp. 22–29, October 2010.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
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11 SlideShare 
13 PdfSR 
B. Mykerjee, L. Heberlein T., and K. Levitt N., "Network Intrusion Detection", IEEE Networks, Vol. 8, No.3, PP.14-26. 1994.
D. Novikov, V. Roman Yampolskiy, and L. Reznik, "Anomaly Detection Based Intrusion Detection", IEEE computer society.2006.
D. Novikov, V. Roman Yampolskiy, and L. Reznik, "Artificial Intelligence Approaches For Intrusion Detection", IEEE computer society.2006.
I. Ahmad, S. Ullah Swati and S. Mohsin, "Intrusions Detection Mechanism by Resilient Back Propagation (RPROP)", European Journal of Scientific Research ISSN 1450-216X Vol.17 No.4, pp.523-531.2007.
J. Bezdek, C., "pattern Recognition with Fuzzy Objective Function Algorithms". Plenum, New York.1981.
J. Shum and A. Heidar Malki, "Network Intrusion Detection System Using Neural Networks", Fourth International Conference on Natural Computation, IEEE computer society.2008.
J., Muna. M. and Mehrotra M., "Intrusion Detection System : A design perspective", 2rd International Conference On Data Management, IMT Ghaziabad, India. 2009.
KDD-cup dataset. http://kdd.ics.uci.edu/data base/ kddcupaa/kddcup.html
Loril D., "Applying Soft Computing Techniques to intrusion Detection", Ph.D thesis, Dep. Of Computer Sce. University of Colorado at Colorado Spring, 2005.
M. Al-Subaie, "The power of sequential learning in anomaly intrusion detection", degree master thesis, Queen University, Canada.2006.
M. Khattab Ali, W. Venus, and M. Suleiman Al Rababaa, "The Affect of Fuzzification on Neural Networks Intrusion Detection System", IEEE computer society.2009.
M. Moradi and M. Zulkernine, "A Neural Network based system for intrusion detection and classification of attacks", Queen University, Canada.2004.
M. Panda, and M. Patra, “Building an efficient network intrusion detection model using Self Organizing Maps", proceeding of world academy of science, engineering and technology, Vol. 38. 2009.
M. Vallipuram and B. Robert, "An Intelligent Intrusion Detection System based on Neural Network", IADIS International Conference Applied Computing.2004.
P. Kukie?ka and Z. Kotulski, "Analysis of Different Architectures of Neural Networks for Application in Intrusion Detection Systems", Proceedings of the International Multiconference on Computer Science and Information Technology, IEEE, pp. 807– 811.2008.
P. Kukielka and Z. Kotulski, "Analysis of different architectures of neural networks for application in intrusion detection systems", proceeding of the international multiconference on computer science and information technology, pp. 807-811.2008.
S. Jimmy and A. Heidar, "Network Intrusion Detection System using Neural Networks", IEEE computer society.2008.
S. Lília de Sá, C. Adriana Ferrari dos Santos, S. Demisio da Silva, and A. Montes, "A Neural Network Application for Attack Detection in Computer Networks", Instituto Nacional de Pesquisas Espaciais – INPE, BRAZIL.2004.
S. Mukkamala, H. Andrew Sung, and A. Abraham, "Intrusion detection using an ensemble of intelligent paradigms", Journal of Network and Computer Applications 28. pp167–182.2005.
T. Zhou and LI Yang, "The Research of Intrusion Detection Based on Genetic Neural Network", Proceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, Hong Kong, IEEE.2008.
W. Jung K., "Integration Artificial Immune Algorithms for Intrusion Detection", dissertation in University of London, PP.1-5.2002.
Y. John and R. Langari, "Fuzzy Logic intelligence, control, and information", Publish by Dorling Kindersley, India, pp.379-383.2006.
Miss muna mhammad taher jawhar
jamia millia islamia - India
Mr. Monica Mehrotra
- India