Computer Science ›› 2025, Vol. 52 ›› Issue (3): 391-399.doi: 10.11896/jsjkx.240100151

• Information Security • Previous Articles     Next Articles

Tor Network Path Selection Algorithm Based on Similarity Perception

SUI Jiaqi1, HU Hongchao1,2, SHI Xin2, ZHOU Dacheng2, CHEN Shangyu2   

  1. 1 School of Cyber Science and Engineering,Zhengzhou University,Zhengzhou 450001,China
    2 Institute of Information Technology,University of Information Engineering,Zhengzhou 450001,China
  • Received:2024-01-19 Revised:2024-10-25 Online:2025-03-15 Published:2025-03-07
  • About author:SUI Jiaqi,born in 2000,postgraduate.His main research interests include cyber security and anonymous communication.
    HU Hongchao,born in 1982,professor,Ph.D supervisor.His main research interests include cloud computing security and cyber security.
  • Supported by:
    Regional Innovation and Development Joint Fundof the National Natural Science Foundation of China(U22A2001) and National Natural Science Foundation of China(62072467).

Abstract: Due to the low threshold construction conditions and open participation mechanism of Tor,attackers can conduct Sybil assaults on Tor networks by controlling a significant number of malicious Sybil nodes,posing a serious threat to user privacy.One class ofmethod defenses by identifying malicious nodes.This class of security suffers from a lack of accuracy in evaluating node similarities and challenges in recognizing malicious nodes that have targeted concealment.The other class protects by strengthening the security of the Tor path selection algorithm,which has issues such as being unable to withstand repeated Sybil attacks and finding it challenging to satisfy the needs of both performance and security.To make up for the vulnerability problem of the existing defense methods themselves,it is proposed to apply the malicious node identification methods and path selection algorithms comprehensively.First,the information of relay nodes is collected from multiple data sources,and the data from multiple sources are verified,filtered,and fused to improve the security at the data level.Second,the selection tendency of dependable nodes with long-term bandwidth stability is somewhat increased by the optimization of bandwidth measurements based on historical data,increasing the cost of deploying malicious Sybil nodes for attackers.Then,the relay node similarity assessment method is optimized,and a nearest-neighbor sorting algorithm based on aggregated similarity scores is proposed to improve the accuracy of the node similarity analysis.Finally,the optimized similarity assessment method is integrated into the path selection algorithm design,and a path selection algorithm based on similarity perception is proposed.Experimental results show that the algorithm not only shows better defense effect against multiple Sybil attacks,but also ensures that the performance requirements of the link are met.

Key words: Anonymous communication, Tor, Sybil attack, Path selection algorithm

CLC Number: 

  • TP393.08
[1]KARUNANAYAKE I,AHMED N,MALANEY R,et al.De-anonymisation Attacks on Tor:A Survey[J].IEEE Communications Surveys & Tutorials,2021,23(4):2324-2350.
[2]NASR M,BAHRAMALI A,HOUMANSADR A.Deepcorr:Strong flow correlation attacks on tor using deep learning[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.2018:1962-1976.
[3]KOHLS K,JANSEN K,RUPPRECHT D,et al.On the Challenges of Geographical Avoidance for Tor[C]//NDSS.2019.
[4]SUN Y X,EDMUNDSON A,VANBEVER L,et al.RAPTOR:Routing attacks on privacy in Tor[C]//24th USENIX Security Symposium(USENIX Security 15).2015:271-286.
[5]RIMMER V,PREUVENEERS D,JUAREZ M,et al.Automated website fingerprinting through deep learning[J].arXiv:1708.06376,2017.
[6]SIRINAM P,IMANI M,JUAREZ M,et al.Deep fingerprinting:Undermining website fingerprinting defenses with deep learning[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.2018:1928-1943.
[7]LI S,GUO H J,HOPPER N.Measuring information leakage in website fingerprinting attacks and defenses[C]//Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security.2018:1977-1992.
[8]WINTER P,ENSAFI R,LOESING K,et al.Identifying andcharacterizing Sybils in the Tor network[C]//25th USENIX Security Symposium(USENIX Security 16).2016:1169-1185.
[9]ZHANG W Y.Research and Implementation of Tor Network path selection strategy against sybil attacks[D].Beijing:Beijing University of Posts and Telecommunications,2020.
[10]FENG Q,XIA Y M,YAO W B,et al.Malicious Relay Detection for Tor Network Using Hybrid Multi-Scale CNN-LSTM with Attention[C]//2023 IEEE Symposium on Computers and Communications(ISCC).IEEE,2023:1242-1247.
[11]JOHNSON A,JANSEN R,JAGGARD A D,et al.Avoiding the man on the wire:Improving Tor’s security with trust-aware path selection[J].arXiv:1511.05453,2017.
[12]LV M,ZHU Y F,LIN W.Dynamic Routing Algorithm Based on Bandwidth of Anonymous Network[J].Journal of Information Engineering University,2019,20(5):591-596.
[13]HANLEY H,SUN Y X,WAGH S,et al.DPSelect:A Differential Privacy Based Guard Relay Selection Algorithm for Tor[J].Proceedings on Privacy Enhancing Technologies,2019,2:166-186.
[14]ZHAO H,WANG L M,SHEN T H,et al.Survey on Anonymity Metrics in Communication Network[J].Journal of Software,2021,32(1):218-245.
[15]EVERS B,HOLS J,KULA E,et al.Thirteen years of Tor attacks[EB/OL].https://github.com/Attacks-on-Tor/Attacks-on-Tor/blob/master/Thirteen%20years%20of%20Tor%20Atta-cks.pdf.
[16]YE L,YU X Z,ZHAO J D,et al.Deciding your own anonymity:User-oriented node selection in I2P[J].IEEE Access,2018,6:71350-71359.
[17]YANG M,ZHANG Y T,LUO J Z.Randomized bandwidthweighted node selection algorithm for anonymous communication[J].Journal of Southeast University(Natural Science Edition),2010,40(6):1169-1173.
[18]JOHNSON A,WACEK C,JANSEN R,et al.Users get routed:Traffic correlation on Tor by realistic adversaries[C]//Procee-dings of the 2013 ACM SIGSAC Conference on Computer & Communications Security.2013:337-348
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