当前Web追踪领域主要使用浏览器指纹对用户进行追踪.针对浏览器指纹追踪技术存在指纹随时间动态变化、不易长期追踪等问题,提出一种关注节点和边缘特征的改进图采样聚合算法(An Improved Graph SAmple and AGgregatE with Both Node and Edge Features,NE-GraphSAGE)用于浏览器指纹追踪.首先以浏览器指纹为节点、指纹之间特征相似度为边构建图数据.其次对图神经网络中的GraphSAGE算法进行改进使其不仅能关注节点特征,而且能捕获边缘信息并对边缘分类,从而识别指纹.最后将NE-GraphSAGE算法与Eckersley算法、FPStalker算法和LSTM算法进行对比,验证NE-GraphSAGE算法的识别效果.实验结果表明,NE-GraphSAGE算法在准确率和追踪时长上均有不同程度的提升,最大追踪时长可达80天,相比其他3种算法性能更优,验证了 NE-GraphSAGE算法对浏览器指纹长期追踪的能力.
Browser Fingerprint Tracking Based on Improved GraphSAGE Algorithm
The current Web tracking field mainly uses browser fingerprint to track users,and for the problems of browser finger-print tracking technology such as dynamic changes of fingerprint over time and the difficulty of long-term tracking,an improved graph sampling aggregation algorithm NE-GraphSAGE is proposed for browser fingerprint tracking.Firstly,the graph data is constructed using browser fingerprint as nodes and feature similarity between fingerprints as edges.Secondly,the GraphSAGE algorithm in graph neural networks is improved to not only focus on node features,but also capture edge information and classify edges to identify fingerprint.Finally,the NE-GraphSAGE algorithm is compared with Eckersley algorithm,FPStarker algorithm,and LSTM algorithm to verify the recognition effect of NE-GraphSAGE algorithm.Experimental results show that the NE-GraphSAGE algorithm has different degrees of improvement in accuracy and tracking time,and the maximum tracking time is up to 80days.Compared with the other three algorithms,the NE-GraphSAGE algorithm has better performance,verifying its ability to track browser fingerprint for a long time.
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