A Fingerprint Identification Method of Multi-Page and Multi-Tag Targeting Tor Website
Tor anonymous communication system is often used by criminals to engage in darknet criminal activities.Tor webpage fingerprint identification technology provide technical means for darknet supervision.Aiming at the problem of poor practicality of single label tor website recognition technology in the process of network supervision,this paper proposed a multi-page and multi-tag tor fingerprint identification method.Firstly,standard particle swarm optimization and K nearest neighbor(KNN)were optimized and combined,and KNN based on adaptive PSO(APSO-KNN)model was proposed for successive multi-tag website segmentation.Then,1D CNN combined with self-attention mechanism(SA-1DCNN)model was used to classify content of website fragments.Finally,APSO-KNN memory scoring mechanism was used to select suboptimal segmentation point of website that failed to be identified.Experimental results show APSO-KNN uses particle search mechanism instead of exhaustive traversal mechanism to find the split point.It can achieve 96.3%segmentation accuracy,and efficiency is significantly improved compared with the traditional KNN algorithm.Deep learning model SA-1DCNN is better than machine learning model in terms of resist website segmentation error and can achieve 96.1%identification accuracy.
the onion routerwebsite fingerprintparticle swarm optimizationweighted K-nearest neighbor
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