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2014 | z. 68 | 377--386
Tytuł artykułu

Intrusion Detection System Based on Neural Networks

Treść / Zawartość
Warianty tytułu
System wykrywania włamań opartych na sieciach neuronowych
Języki publikacji
EN
Abstrakty
W artykule zaprezentowano podejście do projektowania detektora ataków komputerowych za pomocą sieci neuronowej i kwantyzacji wektorowej. Bazuje ono na ulepszonej metodzie hierarchicznej klasyfikacji ataków komputerowych i kompresji informacji za pomocą analizy głównych składowych i łączenia sieci neuronowych detektorów.(abstrakt oryginalny)
EN
Designing the neural network detector of attacks using the vector quantization is considered in this paper. It's based on improved method for hierarchical classification of computer attacks and the information compression using the principal component analysis and combining the neural network detectors.(original abstract)
Rocznik
Numer
Strony
377--386
Opis fizyczny
Twórcy
  • Silesian University of Technology, Poland
  • Ternopil National Economic University, Ukraine
Bibliografia
  • 1. Signature-based Multi-Layer Distributed Intrusion Detection System using Mobile Agents/MueenUddin, Azizah Abdul Rehman, NaeemUddin [et al.]//International Journal of Network Security, Vol. 15, No. 2, 2013, p. 79-87.
  • 2. Anomaly-based network intrusion detection: Techniques, systems and challenges/P. Garcia-Teodoro J., Diaz-Verdejo G., Macia-Fernandez [et al.]//Computers and Security, Vol. 28, No. 1-2, 2009, p. 18-28.
  • 3. Salour M.: Dynamic two-layer signature-based ids with unequal databases/M. Salour, X. Su//In Fourth International Conference on Information Technology, 2007, p. 77-82.
  • 4. Wagner D.: Intrusion detection via static analysis/D. Wagner, D. Dean//Proceedings of the 2001 IEEE Symposium on Security and Privacy, 2001, p. 156.
  • 5. Yeung D.-Y.: Host-based intrusion detection using dynamic and static behavioral models/ D.-Y. Yeung, Y. Ding // Pattern Recognition, Vol. 36(1), 2003, p. 229-243.
  • 6. Debar H., Becker M., Siboni D.: A neural network component for an intrusion detection system, in Proc. of the IEEE Symposium on Research in Security and Privacy, Oakland, CA, May 1992, p. 1-11.
  • 7. Gaffney J. Jr, Ulvila J.: Evaluation of intrusion detectors: A decision theory approach, in Proceedings of IEEE Symposium on Security and Privacy, (S&P), 2001, p. 50-61.
  • 8. Ragsdale D.J., Carver C.A., Humphries J.W., Pooch U.W.: Adaptation Techniques for Intrusion Detection and Intrusion Response Systems, in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Nashville, Tennessee, October 8-11, 2000, p. 2344-2349.
  • 9. Spafford E.H., Zamboni D.: Intrusion detection using autonomous agents, in Computer Networks, Vol. 34, Issue 4, October 2000, p. 547-570.
  • 10. Ruck D., Rogers S., Kabrisky M., Oxley M., Suter B.: The multilayer perceptron as an approximation to a Bayes optimaldiscriminant function, IEEE Transactions on Neural Networks, Vol. 1, No. 4, 1990, p. 296-298.
  • 11. Komar M.: Methods of artificial neural networks for network intrusion detection// Proceedings of the Seventh International Scientific Conference "Internet - Education - Science - 2010", Vinnitsa (Ukraine), 2010, p. 410-413.
  • 12. Komar M.: System for analyzing network traffic to detect computer attacks. Herald Brest State Technical University. Physics, mathematics, computer science, No. 5, 2010, p. 14-16.
  • 13. Kohonen T.: Sell-organised formation of topologically correct feature maps. Biological Cybernetics, No. 43, 1982, p. 59-69.
  • 14. Golovko V.: Neural Networks: training, models and applications/V. Golovko, A. Galushkin. Radiotechnika, Moscow 2001, p. 256.
  • 15. Komar M.: Intelligent System for Detection of Networking Intrusion/M. Komar, V. Golovko A., Sachenko S., Bezobrazov//Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS-2011): IEEE international conference, 15-17 September 2011, Prague, Czech Republic 2011, p. 374-377.
  • 16. Tavallaee M., Bagheri E., Wei Lu, Ali, A. Ghorbani: A Detailed Analysis of the KDD CUP 99 Data Set"/Proceedings of the 2009 IEEE Symposium on Computational Intelligence in Security and Defense Applications (CISDA 2009). DOI: 10.1109/CISDA.2009.5356528. Publication Year 2009, p. 1-8.
  • 17. KDD Cup 1999 Data / The UCI KDD Archive, Information and Computer Science. - University of California, Irvine, 1999.
  • 18. Gorban B., Kegl D., Wunsch A., Zinovyev (Eds.), Principal Manifolds for Data Visualisation and Dimension Reduction, LNCSE 58, Springer, Berlin - Heidelberg - New York 2007.
  • 19. Komar M.: Method of Aggregate Classifier Construction for Hierarchical Classification of Computer Attacks/V. Golovko, O. Lyashenko, A. Sachenko//CAD in Machinery Design. Implementation and Educational Issues (CADMD 2012): international conference, 11-13 October 2012: proceedings. Lviv, Ukraine, 2012, p. 80-82.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171343327

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