| Title | Constructing Browser Fingerprint Tracking Chain Based on LSTM Model |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Li, X., Cui, X., Shi, L., Liu, C., Wang, X. |
| Conference Name | 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC) |
| Keywords | browser fingerprint, browser fingerprint evolution, browser fingerprint tracking chain, browser security, Browsers, compositionality, Computer architecture, Fingerprint recognition, Human Behavior, input feature vector, LSTM, LSTM model, Metrics, online front-ends, pubcrawl, recurrent neural nets, Recurrent neural networks, resilience, Resiliency, security of data, Stability analysis, stateless tracking technique, time series, Time series analysis, track Web attackers, Tracking, Training, training set |
| Abstract | Web attacks have increased rapidly in recent years. However, traditional methods are useless to track web attackers. Browser fingerprint, as a stateless tracking technique, can be used to solve this problem. Given browser fingerprint changes easily and frequently, it is easy to lose track. Therefore, we need to improve the stability of browser fingerprint by linking the new one to the previous chain. In this paper, we propose LSTM model to learn the potential relationship of browser fingerprint evolution. In addition, we adjust the input feature vector to time series and construct training set to train the model. The results show that our model can construct the tracking chain perfectly well with average ownership up to 99.3%. |
| DOI | 10.1109/DSC.2018.00038 |
| Citation Key | li_constructing_2018 |
Constructing Browser Fingerprint Tracking Chain Based on LSTM Model