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Securing BitTorrent using a new reputation-based trust management system

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Abstract

Nowadays, BitTorrent as a means of sharing files has become highly popular among internet users. However, due to the open nature of BitTorrent protocol and lack of any security mechanism, number of attacks against BitTorrent has significantly increased. Sybil, Collusion, Lying-Piece, Fake-Block, and Chatty-Peer are attack types which have been considered in this paper to secure BitTorrent against them. These attacks can decrease the download performance of BitTorrent clients considerably. In this paper a new reputation based trust management system to cover aforementioned attack types is presented. The proposed approach calculates a local score at peers and a global score at the tracker for each peer. First, peers are sorted according to their cumulative score at the tracker and then top 10 % of these peers are used to determine other peers global score. These local and global scores are used to find attackers. In addition, a novel formula has been utilized to calculate peers local score. Using the global score concept makes our mechanism robust and swift to detect collusion attack which has not been considered in most of similar previous works. In order to evaluate the effectiveness of the proposed system, several simulation and real experiments in the Emulab testbed were performed. The outcomes indicate that our method is highly effective in detection of rogue peers and Free-Riders; moreover, performance of honest peers has significantly improved.

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References

  1. BitTorrent. http://www.bittorrent.com/. Accessed Dec. 2011

  2. Ipoque, “Internet study 2007: Data about P2P, VoIP, Skype, file hosters like Rapidshare and streaming services like YouTube”. http://www.ipoque.com/resources/internet-studies/internet-study-2007/,2007. Accessed Dec. 2011

  3. Banerjee A, Faloutsos M, Bhuyan L (2007) “Is someone tracking P2P users?”. Proc IFIP NETWORKING, Atlanta, GA

  4. BitTorrent servers under attack. http://en.wikipedia.org/wiki/Torrent_poisoning/. Accessed Dec. 2011

  5. Ziptorrent blacklist. http://torrentfreak.com/ziptorrent-pollutes-and-slows-down-popular-torrents/. Accessed Dec. 2011

  6. Kong J, Cai W, Wang L, Zhao Q (2010) “A study of pollution on BitTorrent”, Proc. The 2nd International Conference on Computer and Automation Engineering, Singapore, Feb. 2010

  7. Konrath MA, Barcellos MP, Mansilha RB (2007)” Attacking a swarm with a band of liars: evaluating the impact of attacks on bittorrent”, Proc. IEEE P2P, Galway, Ireland

  8. Shin K, Reeves DS, Rhee I (2009) “Treat-before-trick: Free-riding prevention for BitTorrent-like peer-to-peer networks”, Proc. IEEE International Symposium on Parallel&Distributed, pp 1–12

  9. Locher T, Moor P, Schmid S, Wattenhofer R (2006) “Free riding in bittorrent is cheap”, Proc Hot-Nets

  10. Piatek M, Isdal T, Anderson T, Krishnamurthy A, Venkataramani A (2007) “Do incentives build robustness in bittorrent?”, Proc. 4th USENIX Symposium on Networked Systems Design & Implementation, pp 1–14

  11. Maini S (2006) “A Survey Study on Reputation-Based Trust Management in P2P Networks”, Technical Report, Department of Computer Science; Kent State University

  12. Cohen B (2003) “Incentives build robustness in BitTorrent”, Proc. 1st Workshop on Economics of Peer-to-Peer Systems

  13. Levin D, LaCurts K, Spring N, Bhattacharjee B (2008) “BitTorrent is an Auction: Analyzing and Improving BitTorrent’s Incentives”, Proc. SIGCOMM’08, Seattle, Washington, USA

  14. Blaze M, Feigenbaum J (1996) “Decentralized Trust Management”, Proc. IEEE Symposium on Security and Privacy

  15. Kagal L, Cost S (2001) “A framework for distributed trust Management”, Proc. Second Workshop on Norms and Institutions in MAS, Autonomous Agents

  16. Sabater J, Sierra C (2002) “Reputation and social network analysis in multi-agent systems”, Proc. First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy

  17. Pujol J, Sanguesa R (2002) “Extracting reputation in multi agent systems by means of social network topology”, Proc. First International Joint Conference on Autonomous Agents and Multi-Agent Systems, Bologna, Italy

  18. Kamvar SD, Schlosser MT, Garcia-Molina H (2003) “The Eigen Trust algorithm for reputation management in p2p networks”. Proc. 12th International World Wide Web Conference

  19. Hu J, Li X, Zhou B, Li Y (2010) “SECTrust: A Secure and Effective Distributed P2P Trust Model”, Proc. IITSI, pp 34–38

  20. Stakhanova N, Ferrero S, Wong J, Cai Y (2004) “A reputation-based trust management in peer-to-peer network systems,” Proc. International Workshop on Security in Parallel and Distributed Systems, San Francisco, CA

  21. Singh A, Liu L (2004) “TrustMe: Anonymouss management of trust relationships in decentralized P2P systems”, Proc. The Third IEEE International Conference on Peer-to-Peer Computing, Linkopings

  22. Shah P, Pâris J-F (2007) “Incorporating trust in the BitTorrent protocol”, Proc. of SPECTS, Paris

  23. Chen H, Ye Z, Liu W, Wang C (2009) “Fuzzy Inference Trust in P2P Network Environment”, Proc. International Workshop on Intelligent Systems and Applications, Wuhan, pp 1–4

  24. Liu F, Ding Y (2007) “Ecological Network-Inspired Trust Management Model of P2P Networks”, Proc. Second Workshop on Digital Media and its Application in Museum & Heritages, pp 297–302

  25. Gupta M, Judge P, Ammar MA (2003) “A reputation system for peer-to-peer networks”. Proc. NOSSDA V

  26. Gheorghe G, Cigno RL, Montresor A “Security and Privacy Issues in P2P Streaming Systems: A Survey”, Springer. Peer-to-Peer Networking and Applications, 2010, Springger New York, pp 1–17

  27. Douceur JR (2002) “The Sybil attack”, Proc. 1st International Work-shop on Peer-to-Peer Systems, Cambridge, MA, USA, pp 251–260

  28. Dhungel P, Wu D, Schonhorst B, Ross KW (2008) ”A measurement study of attacks on BitTorrent leechers”, Proc. IPTPS, Tampa Bay, FL, USA

  29. Conner W, Nahrstedt K, Gupta I (2006) “Preventing DoS attacks in peer-to-peer media streaming systems”, Proc. 13th Annual Conference on Multimedia Computing and Networking (MMCN’06), San Jose, CA, USA

  30. Yang J, Li Y, Huang B, Ming J (2008) “Preventing DDoS attacks based on credit model for P2P streaming system”, Proc. 5th international conference on Autonomic and Trusted Computing(ATC’08), Berlin, Heidelberg, pp 13–20

  31. Kohno T, Broido A, Claffy K (2005) Remote physical device fingerprinting. IEEE Trans Dependable Secure Comput 2(2):93–108

    Article  Google Scholar 

  32. Bazzi R, Konjevod G (2005) “On the establishment of distinct identities in overlay networks”, Proc. ACM Symposium on Principles of Distributed Computing, Las Vegas, NV

  33. Emulab - Network Emulation Testbed. www.emulab.net. Accessed Aug. 2011

  34. Yang W, Abu-Ghazaleh N (2005) “GPS: a general Peer-to-Peer simulator and its use for modeling BT”, Proc. 13 Int. Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems, Atlanta, GA

  35. Zegura EW, Calvert KL, Bhattacharjee S (1996) How to model an internetwork. Proc IEEE Infocom USA 2:594–602

    Google Scholar 

  36. The Hunting of the Snark Project, http://klomp.org/snark/, Accessed Oct. 2010

  37. Indie Band Tops a Million Downloads, Breaks BitTorrent Record. http://torrentfreak.com/indie-band-tops-a-million-downloads-breaks-bittorrent-record-110317/. Accessed Aug. 2011

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Acknowledgments

This research has been done by financial support of Shahid Beheshti University research chancellor under Contract No.: 600/537-90/3/30.

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Authors and Affiliations

  1. Department of Computer Engineering, Faculty of Electrical and Computer Engineering, Shahid Beheshti University, G. C., Evin., Tehran, Iran

    Behrooz Shafiee Sarjaz & Maghsoud Abbaspour

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  1. Behrooz Shafiee Sarjaz
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  2. Maghsoud Abbaspour
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Corresponding author

Correspondence to Maghsoud Abbaspour.

Appendix A Calculation of Sn, Sp, Kg and K f parameters

Appendix A Calculation of Sn, Sp, Kg and K f parameters

In order to estimate Sn parameter we collected a huge amount of data samples. By plotting data and fitting probability curves, we discovered that fake peers’ scores follow a normal distribution. As a result, in order to estimate the average score of fake peers given by honest peers, statistical confidence interval estimation was used. A %85 confidence interval for the average score of fake peers was calculated as following:

$$ \matrix{ {{\text{A}}\,{1}00 * \left( {{1} - \alpha } \right)\,{\text{confidence}}\,{\text{interval}}\,{\text{for}}\,{\text{average}}\,{\text{of}}\,{\text{data}}\left( \mu \right):} \\ {\mu \in (\mathop{x}\limits^{\_ } - {z_{{1 - \frac{\alpha }{2}}}}(n - 1)\frac{s}{{\sqrt {n} }},\mathop{x}\limits^{\_ } + {z_{{1 - \frac{\alpha }{2}}}}(n - 1)\frac{s}{{\sqrt {n} }})} \\ }<!end array> $$

Where μ is average score of fake peers, \( \overline x \) and s are standard average and deviation of samples, n is number of samples and (1-α) equals 0.85. Replacing these values, we have μ Є (−0.9223, −0.7023) confidence interval for average score of fake peers. By a similar analysis to Sn, the following intervals for other parameters were estimated.

Parameter

Confidence-Interval

Sp

(+0.6703,+0.8803)

Kg

(+0.6326,+0.8504)

Kf

(+0.7806,+0.8308)

1.1 Appendix B Calculation of local and global trust score thresholds

In order to find optimal values for the local and global trust score thresholds, we ran several simulations of a swarm with 100 peers including 25 malicious peers of different types. These thresholds can have a value in the range of [-1, 1]; however, we know that if peer A wants to unchoke peer B, peer B should have at least a positive score. Therefore, we limited the possible range for these parameters to [0, 1]. In addition, as it was discussed in the section 4.3 the excessively high values for these parameters can cause performance downfall in the swarm; consequently, the range was limited to [0, 0.7]. All the simulations parameters except the local and global trust score thresholds were same as those in the section 5. Figure 7.a and b show the results of simulation for these parameters. As these figures indicate, 0.2 is the best value for the local trust score threshold and 0.5 is the best for the global trust score threshold.

Fig. 7
figure 7

a the average download speed of honest peers with different values for the local trust score thresholds and same global trust score thresholds. b the average download speed of honest peers with different values for the global trust score thresholds and same local trust score threshold

Full size image

Moreover, in order to evaluate the effect of different values of local and global thresholds on the robustness of proposed system and the number of fake peers selected as super-peer, we used some of the combinations of these thresholds. We tested six different combinations of 0.2, 0.3 for local score threshold and 0.4, 0.5, and 0.6 for global trust score threshold. The simulations were performed in GPS simulator in a swarm of 100 peers with 40 malicious peers of different types. The following table shows the number of malicious peers selected as the super peer after 15 min during which system reaches a steady state. As the results indicate, the proposed mechanism is not highly dependent on these thresholds and all the values for these thresholds within the given ranges have a similar effect on the robustness of system.

Local & Global Trust Score

Number of malicious peers selected as super-peer

Local: 0.2 Global:0.4

1

Local: 0.2 Global:0.5

2

Local: 0.2 Global:0.6

1

Local: 0.3 Global:0.4

1

Local: 0.3 Global:0.5

1

Local: 0.3 Global:0.6

1

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Shafiee Sarjaz, B., Abbaspour, M. Securing BitTorrent using a new reputation-based trust management system. Peer-to-Peer Netw. Appl. 6, 86–100 (2013). https://doi.org/10.1007/s12083-012-0141-y

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