TSA-WF: Exploring the Effectiveness of Time Series Analysis for Website Fingerprinting

Procedure 1) First, obtain raw datasets from the Github of the Jin et al. Paper. - These should be placed into ../datasets/no_defense/test and ../datasets/no_defense/train 2) (Optional) Create synthetic multi-tab merged traces using create_merged_traces.ipynb. This will produce ../datasets/no_defense/train_X-tab and ../datasets/no_defense/test_X-tab files. Here, you can configure the number of tabs and overlap between tabs. 3) (Optional) Datasets can then be combined into a single, large file with merge-files.ipynb. 4) Then, generate shapelet data from the merged datasets with generate-shapelets.ipynb. This will produce shapelets under ../datasets/no_defense/shapelets. Note that regardless of the number of tabs used, the same shapelets will be created from the original data obtained in 1). 5) Then distances between dataset and shapelets is computed using compute-distances.ipynb. This will produce distance computations within the ../results/data/X/ folder of the format type=(pos,neg)_centroid_id=(0,1)_tabs=(3,5,7)_dataset=(train,test). 6) Finally, the score for evaluation (Shown in Table 4) can be generated with merge-results_classify.ipynb. This is a very simple and fast RandomForestClassifier, replace with classifier of your choice. The Accuracy score will be printed in the notebook. 7) (Optional) To find trace locations in multi-tab traces (From Section 5.4) use check_distances.ipynb 8) (Optional) Defended traces can be generated by repeating steps 2) - 7) after applying the code provided by the defense's Github Repositories Note: Charts in the paper were created in check_distances_chart.ipynb and graph_figures.ipynb.