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  5. Traffic analysis of anonymity protocol using hidden Markov model based on the model confidence

Traffic analysis of anonymity protocol using hidden Markov model based on the model confidence

Qasim Abbood Mahdi
Системи обробки інформації. — 2018. — № 4(155). – С. 66-76.
UDK 621.391
Article language: english
Annotations languages:


Annotation: The issue of increasing the confidentiality and stealth of users on the Internet is the most pressing issue of the day. One way to increase the secrecy of using Internet services is to install the Tor software, which protects itself from the "data flow analysis" is a type of network surveillance that threatens the privacy of users, the confidentiality of business contacts and communications implemented through routing network traffic over a distributed network of servers running volunteers from around the world that does not allow the external observer to monitor the user's Internet connection, find out which sites were visited, and also does not allow the site to know the physical location of the user. However, the software in question has vulnerabilities that result in the loss of personal user freedom. The author, through the application of general scientific methods such as analysis and synthesis, identified a list of vulnerabilities and their importance for the confidentiality of the Tor software. The author carried out the simulation of the Tor software by devices of the experimental environment and the construction of experimental procedures based on the used mathematical apparatus of the Markov chains. The results of the experiment indicate the necessity to determine the validity of the model for analysis of the anonymity protocol. In the course of this research, an algorithm for testing the anonymity of Tor software users was developed, which allows to identify possible sources of personal information of users. The effectiveness of the proposed modeling trust algorithm was demonstrated by calculating the value of a training set of data necessary for outputting a wireless access protocol, a proxy through Tor.


Keywords: Tor anonymity network, Internet security, intrusion detection system, traffic survey

References

1. AlSabah, M., Bauer, K., Elahi, T. and Goldberg, I. (2013), The path less travelled: Overcoming Tor’s bottlenecks with traffic splitting, Privacy Enhancing Technologies Symposium (PETS), pp. 143-163, Springer.
2. Chaabane, A., Manils, P. and Kaafar, M.A. (2010), Digging into anonymous traffic: A deep analysis of the Tor anonymizing network, IEEE Network and System Security (NSS).
3. Beasley, C., Zhong, X., Deng, J., Brooks, R. and Kumar Venayagamoorthy, G. (2014), A Survey of Electric Power Synchrophasor Network Cyber Security, Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), 2014 IEEE PES, pp. 1-5. https://doi.org/10.1109/ISGTEurope.2014.7028738.
4. Aras, R. and Dutech, A. (2010), An Investigation into Mathematical Programming for Finite Horizon Decentralized POMDPs, Journal of Artificial Intelligence Research, Vol. 37, pp. 329-396. https://dx.doi.org/10.1613/jair.2915.
5. Schwier, J.M., Brooks, R.R., Griffin, C. and Bukkapatnam, S. (2009), Zero knowledge hidden Markov model inference, Pattern Recognition Letters, Vol. 30(14), pp. 1273-1280. https://dx.doi.org/10.1016/j.patrec.2009.06.008.
6. Archer, G.E.B. and Titterington, D.M. (2002), Parameter estimation for hidden Markov chains, Journal of Statistical Planning and Inference, Vol. 108 (1-2), pp. 365-390.
7. Ephraim, Y. and Merhav, N. (2002), Hidden Markov processes, Institute of Electrical and Electronics Engineers. Transactions on Information Theory, Special issue on Shannon theory: perspective, trends, and applications, Vol. 48(6), pp. 1518-1569.
8. Poupart, P. (2005), Exploiting structure to efficiently solve large scale partially observable Markov decision processes: PhD thesis, University of Toronto.
9. Spaan, M.T.J. and Vlassis, N. (2005), Perseus: randomized point-based value iteration for POMDPs, JAIR, Vol. 24, pp. 195-220.
10. Zhong, X., Arunagirinathan, P., Ahmadi, A., Brooks, R., Venayagamoorthy, G.K., Yu, L. and Fu, Y. (2015), Side Channel Analysis of Multiple PMU Data in Electric Power Systems, Power System Conference (PSC), Clemson University, pp. 1-6.
11. Fu, Y. (2017), Using botnet technologies to counteract netowrk traffic analysis: Ph.D. thesis, Clemson University.
12. Rabiner, L.R. and Juang, B.H. (1986), An introduction to hidden markov models, IEEE ASSP Magazine, pp. 4-16.
13. Capp´e, O., Moulines, E., and Ryden, T. (2005), Inference in Hidden Markov Models, Springer Series in Statistics, Springer-Verlag New York, Inc., Secaucus, NJ, USA.
14. Loesing, K., Murdoch, S.J., and Dingledine, R.A. (2010), Case Study on Measuring Statistical Data in the Tor Anonymity Network, Proc. of the Workshop on Ethics in Computer Security Research.

Information about the authors of publication:
Reference:
Qasim Abbood Mahdi, (2018), Traffic analysis of anonymity protocol using hidden Markov model based on the model confidence, Information Processing Systems, Vol. 4(155), pp. 66-76. https://doi.org/10.30748/soi.2018.155.09.