Abstract

This study discusses the problem distributed network attacks, formalized of model of linear kind for differentiate distributed network attacks on the basis of a weight coefficients Structured the formalized mathematical models allow to consider structure of the On network to a basis big percent, a measure of influence of each type of attack that gives the fine chance effectively to design to protect information system taking into account information on threats. Based on classification of information threats, characteristic for distributed network attacks it is offered the formalized models of a linear look for differentiation of attacks on the basis of a method of weight coefficients. By these indicators and coefficients it is possible to define the main types of threats in computer systems allowing to design effectively systems of information security taking into account information threats.

Keywords:

Client-server model of communication, distributed network attacks, formalized mathematical model, mathematical model,

References

1. Aleksander, M.A., M.P. Karpinski and U.O. Yatsykovska, 2012. Features of Denial of Service attacks in information systems. Inform. Math. Methods Simul., 2(2): 129-130.
   
2. Apiecionek, L., J.M. Czerniak and W.T. Dobrosielski, 2015. Quality of services method as a DDoS protection tool. Adv. Intel. Sys. Comput., 323: 225-234.
   CrossRef    
3. Baba, T. and S. Matsuda, 2002. Tracing network attacks to their sources. IEEE Internet Comput., 6(2): 20-26.
   CrossRef    
4. Bhatia, S., D. Schmidt, G. Mohay and A. Tickle, 2014. A framework for generating realistic traffic for Distributed Denial-of-Service attacks and flash events. Comput. Secur., 40: 95-107.
   CrossRef    
5. Bhuyan, M.H., D.K. Bhattacharyya and J.K. Kalita, 2015. An empirical evaluation of information metrics for low-rate and high-rate DDoS attack detection. Pattern Recogn. Lett., 51: 1-7.
   CrossRef    
6. Bu, T., S. Norden and T. Woo, 2004. Trading resiliency for security: Model and algorithms. Proceeding of the 12th IEEE International Conference on Network Protocols, pp: 218-227.
   
7. Dean, D., M. Franklin and A. Stubble?eld, 2001. An algebraic approach to IP traceback. Proceeding of the Network and Distributed System Security Symposium (NDSS), pp: 3-12.
   
8. Deepthi, S., K.S. Hemanth, D. Rajesh and M. Kalyani, 2015. A Novel Approach for DDoS Mitigation with Router. Adv. Intel. Sys. Comput., 308: 701-707.
   CrossRef    
9. Denial-of-Service Attack, 2015. Wikipedia, the Free Encyclopedia. Retrieved from: http://en.wikipedia.org/wiki/Denial-of-service_attack. (Accessed on: February 2, 2015).
   Direct Link
10. Elliott, J., 2000. Distributed denial of service attacks and the zombie ant effect. IT Professional, 2(2): 55-57.
    CrossRef    
11. Hautio, J. and T. Weckstrom, 1999. Denial of Service Attacks. Retrieved from: http://www.hut.Fi/u/tweckstr/hakkeri/DoSpaper.html. (Accessed on: March, 2014).
    Direct Link
12. Hussain, A., J. Heidemann and C. Papadopoulos, 2003. A framework for classifying denial of service attacks. Proceeding of the 2003 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications SIGCOMM, 2003. Karlsruhe, Germany, pp: 99-110.
    CrossRef    PMid:12887285    
13. Ioannidis, J. and S.M. Bellovin, 2002. Implementing pushback: Router-based defense against DDoS attacks. Proceeding of the Network and Distributed System Security Symposium (NDSS, 2002). Reston, VA, USA, pp: 100-108.
    
14. Karpinski, N. and G. Shangytbayeva, 2015. Architecture and program realization of system of detection of network attacks to denial of service. Proceeding of the International Conference on Global Issues in Multidisciplinary Academic Research (GIMAR, 2015). Dubai, UAE, pp: 55.
    
15. Lee, G., 2000. Denial-of-service attacks rip the internet. Computer, 33(4): 12-17.
    CrossRef    
16. Li, M., M. Li and X. Jiang, 2008. DDoS attacks detection model and its application. WSEAS T. Comput., 7(8) 1159-1168.
    
17. Özçelik, I. and R.R. Brooks, 2014. Deceiving entropy based DoS detection. Comput. Secur., 48: 234-245.
    CrossRef    
18. Savage, S., D. Wetherall, A.R. Karlin and T. Anderson, 2000. Practical network support for IP traceback. Proceeding of the Conference on Applications, Technologies, Architectures and Protocols for Computer Communication (SIGCOMM, 2000), pp: 295-306.
    CrossRef    
19. Stone, R., 2000. Centertrack: An IP overlay network for tracking DoS floods. Proceeding of the 9th Conference on USENIX Security Symposium, 9: 15.
    
20. Szczerba, E.V. and M.V. Szczerba, 2012. Development of the system architecture of distributed detection of network attacks such as "Denial of Service". Sci. Herald Omsk. Ser. Appl. Mach. Technol. 3(113): 280-283.
    
21. Szczerba, E.V. and D.A. Volkov, 2013. Development of the system architecture of distributed detection of network attacks such as "Denial of Service". Appl. Discrete Math., pp: 68-70.
    
22. Yang, Z.X., X.L. Qin, W.R. Li and Y.J. Yang, 2014. A DDoS detection approach based on CNN in cloud computing. Appl. Mech. Mater., 513-517: 579-584.
    CrossRef    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.