Tor Network Path Selection Algorithm Based on Similarity Perception

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.