APPLICATION OF FUZZY ARTMAP NETWORK IN INTELLIGENT SYSTEMS OF INVASION DETECTION

The article deals with the organization of intelligent intrusion detection and detection systems. Research in the field of development of information security tools shows that today the most promising and flexible solutions are based on machine learning methods that can prevent damage from intrusions that were not noticed by standard means of combating computer attacks. In the proposed approach, it is proposed to use a sequential reverse search with a return to select significant features and the Fuzzy neural network ARTMAP to detect and diagnose attacks. Network Fuzzy АRTMAP is able to adapt to the dynamics of computer attacks and allows you to recognize intrusions in the information system in real time, without the need to load datasets in batches. This makes it possible to automate the analysis of safety protocols in a continuous mode. The extensive use of ART family networks in intrusion detection tasks makes it possible to consider the search for approaches that improve their performance. In this paper, the control hyperparameters network Fuzzy ARTMAP proposed to adjust automatically with the use of a genetic algorithm According to the results of the computational experiment, the reduced set of characteristics reduces the computation time by 21%. The accuracy of the classification algorithm was 100% and 99.89% for the detection stage and the diagnostic stage, respectively

1. Denning, Dorothy E. An Intrusion Detection Model// Proceedings of the Seventh IEEE Symposium on Security and Privacy, 1986, pp. 119—131

2. Nesteruk G.F. Informatsionnaya bezopasnost' i intellektual'nye sredstva zashchity informatsionnykh resursov / G.F. Nesteruk, L.G. Osovetskiy, A.F. Kharchenko.– SPb.: Izd-vo SPbGUEF, 2003

3. Nesteruk F. G. Instrumental'nye sredstva sozdaniya neyrosetevykh komponent intellektual'nykh sistem zashchity informatsii/ F. G. Nesteruk, I. V. Kotenko// Trudy SPIIRAN. 2013 Vyp. 26. C. 7–25

4. Carpenter, G.A. & Grossberg, S. (2003), Adaptive Resonance Theory, In Michael A. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, Second Edition (pp. 87-90). Cambridge, MA: MIT Press

5. Carpenter G.A., Grossberg S., Reynolds J.H. ARTMAP: Supervised realtime learning and classification of nonstationary data by a self-organizing neural network // Neural Networks. – 1991. – № 4. – Р. 565-588.

6. Carpenter G.A., Grossberg S., Markuzon N., Reynolds J.H., Rosen D.B. Fuzzy ARTMAP: An adaptive resonance architecture for incremental learning of analog maps. // Proc. of the Int. Joint Conf. on Neural Network. 1992

7. Kashirina I.L. Neyrosetevoe modelirovanie formirovaniya klasternoy struktury na osnove setey ART/ Kashirina I.L., L'vovich Ya.E., Sorokin S.O.// Informatsionnye tekhnologii. 2017. T. 23. № 3. S. 228-232

8. Kashirina I.L. Klasterizatsiya nepreryvnogo potoka dannykh na osnove obobshchennoy modeli neyronnoy seti semeystva ART/ I.L. Kashirina, K.A. Fedutinov //Sistemy upravleniya i informatsionnye tekhnologii. - 2018. T. 71. № 1. S. 33-39.

9. Kashirina I. L. Evolyutsionnoe modelirovanie: uchebnoe posobie/ I. L. Kashirina. -Voronezh: IPTs VGU, 2011. -60 s.

10. KDD Cup 1999 Data [Elektronnyy resurs] URL: http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html (data obrashcheniya: 10.08.2018)