CONCEPTION OF A HYBRID AD APTIVE PROTECTIONOF INFORMATION SYSTEMS

Authors

  • Igor V. Kotenko
  • Filipp G. Nesteruk
  • Andrey V. Shorov

DOI:

https://doi.org/10.47839/ijc.12.1.591

Keywords:

Data mining, malware, detection.

Abstract

The paper suggests the conception of a hybrid adaptive protection of information and telecommunication systems which is based on a biometaphor of nervous and neural networks. A top level of a protection system, based on an approach of “nervous system network” is a distributed mechanism for collecting and processing information. We suggest to implement the in formation processes on the low level with the assistance of an “information field” programming. It allows specifying the distributed information fields in the form of neural network software packages.

References

D. Dasgupta, H. Bersini, et al., Artificial Immune Systems and Their Usage, D. Dasgupta (Eds.), translated from English under edit. of А.А. Romanyiukha, М.: FIZMATLIT, 2006, 344 p. (in Russian)

R.M. Khaitov, Physiology of Immune System, Moscow, VINITI RAN, 2001, 223 p. (in Russian)

N.K. Jerne, Towards a network theory of the immune system, Ann. Immunol. (Inst. Pasteur), (125) (1974), pp. 435-441.

G. Miller, P. Todd, S. Hedge, Designing neural networks using genetic algorithms, Proc. 3rd Int. Conf. on Genetic Algorithms, (1989), pp. 379-384.

I.V. Kotenko, F.G. Nesteruk, A.V. Shorov, Methods of computer networks defense on the base of bio-inspired approaches, Voprosi zaschiti informacii, (2) (2012), pp.35-46. (in Russian)

М.Е. Lobashev, Genetics, Leningrad, LGU Publishers, 1969, 357 p. (in Russian)

I.V. Melik-Gaynazya, Information Processes and Reality, Мoscow, Nauka, 1998, 137 p. (in Russian)

Electronic resource. Access mode – URL: http://www.toyotacenter.ru/ (in Russian)

Electronic resource. Access mode – URL: http://rudocs.exdat.com/docs/index-227356.html (in Russian)

Electronic resource. Access mode – URL: http://www.kaspersky.ru/news?id=207733674 (in Russian)

L.B. Booker, D.E. Goldberg, I.E. Holland, Classifier systems and genetic algorithms, Artificial Intelligence, Elsevier, (40) (1989), pp. 235-282.

G. Deffuant, Reseaux Connectionistes Auto-construits, These D'Etat, 1992, 141 p.

M. Dorigo, H. Bersini A comparative analysis of Q-learning and classifier systems, Proc. SAB'94, MIT Press, (1994), pp. 248-255.

S. Fahlman, С. Lebiere, The cascade-correlation learning architecture, Advances in Neural Information Processing System, Morgan Kaufman, (2) (1990), pp. 524-532.

M. Fombellida, Methodes heuristiques et methodes d'optimalisation non contraintes pour l'apprentissage des perceptrons multicouches, Proc. 5th Int. Conf. on Neural Networks and their Application: Neuro-Nimes, (1992), pp. 349-366.

D.E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, 1989, 432 p.

Y. Hirose, K. Yamashita, S. Hijiya, Back-propagation algorithm which varies the number of units, Neural Networks, (4) (1991), pp. 61-66.

J.H. Holland, K.J. Holyoak, R.E. Nisbett, P.R. Thagard, Induction: Processes of Inference, Learning and Discovery, Cambridge: MIT Press, 1986, 386 p.

Т. Salom, H. Bersini, An algorithm for self-structuring neural net classifiers, Proc. 2nd IEEE Conf. on Neural Network (ICNN'94, 1994), pp. 1307-1312.

R.S. Sutton, Reinforcement learning architectures for animats, Proc. 1st SAB Conference (Eds. J.-A. Meyer and S.W. Wilson). MIT Press, (1990), pp. 288-296.

Y. Chen, H. Chen, NeuroNet: An Adaptive Infrastructure for Network Security, International Journal of Information, Intelligence and Knowledge, (1) 2 (2009), pp.143-168.

I.V. Kotenko, А.М. Konovalov, А.V. Shorov, Modeling of botnets and tools of defense against them, Sistemi visokoi dostupnosti, (2) (2011), pp. 107-111. (in Russian)

I.V. Kotenko, А.М. Konovalov, А.V. Shorov, Researchers modeling of botnets and defense against them, Prilojenie k jurnalu “Informacionnie technologii”, (1) (2012), pp. 32. (in Russian)

I.V. Kotenko, А.V. Shorov, F.G. Nesteruk, Analysis of bio-inspired approaches for defense of computer systems and networks, Trudy SPIIRAN, (3) 18 (2011), pp. 19-73. (in Russian)

I. Kotenko, A. Konovalov, A. Shorov, Agent-based Modeling and Simulation of Botnets and Botnet Defense, Conference on Cyber Conflict. Proceedings 2010. CCD COE Publications. Tallinn, Estonia, (June 15-18, 2010), pp. 21-44.

I. Kotenko, A. Konovalov, A. Shorov, Agent-based simulation of cooperative defense against botnets, Concurrency and Computation: Practice and Experience, (24) 6 (2012), pp. 573-588.

F.G. Nesteruk, А.V. Suhanov, L.G. Nesteruk, G.F. Nesteruk, Adaptive Means of Information Systems Safety Supplying, Monograph. SPb.: Polytechnic University Publishing, 2008, 626 p. (in Russian)

J.B. Dennis, J.B. Fossin, J.P. Linderman, Scheme of data flow, Teoriya programmirivaniya, Novosibirsk: VC SО АN SSSR, (1972), Part. 2. pp. 7-43. (in Russian)

G.F. Nesteruk, М.S. Kupriyanov, F.G. Nesteruk, About developing of language means for neural networks structure programming, Proceedings of V International Conference SCM’2002. SPb, (2002), Vol. 2. pp. 52-55. (in Russian)

F.G. Nesteruk, L.G. Nesteruk, G.F. Nesteruk, Application of the Formal Model for Describing Processes of Adaptive Information Security in Computer-aided Systems, Automation and Remote Control, (70) 3 (2009), pp. 491-501.

А.А. Chechulin, I.V. Kotenko, Combining of defense tools against scanning in computer networks, Informacionno-upravliauschie sistemi, (12) (2010), pp. 21-27. (in Russian)

Downloads

Published

2014-08-01

How to Cite

Kotenko, I. V., Nesteruk, F. G., & Shorov, A. V. (2014). CONCEPTION OF A HYBRID AD APTIVE PROTECTIONOF INFORMATION SYSTEMS. International Journal of Computing, 12(1), 86-98. https://doi.org/10.47839/ijc.12.1.591

Issue

Section

Articles