High Precision Open-World Website Fingerprinting

Traffic analysis attacks to identify which web page a client is browsing, using only her packet metadata --- known as website fingerprinting (WF) --- has been proven effective in closed-world experiments against privacy technologies like Tor. We want to investigate their usefulness in the real open world. Several WF attacks claim to have high recall and low false positive rate, but they have only been shown to succeed against high base rate pages. We explicitly incorporate the base rate into precision and call it r-precision. Using this metric, we show that the best previous attacks have poor precision when the base rate is realistically low; we study such a scenario (r = 1000), where the maximum r-precision achieved was only 0.14. To improve r-precision, we propose three novel classes of precision optimizers that can be applied to any classifier to increase precision. For r = 1000, our best optimized classifier can achieve a precision of at least 0.86, representing a precision increase by more than 6 times. For the first time, we show a WF classifier that can scale to any open world set size. We also investigate the use of precise classifiers to tackle realistic objectives in website fingerprinting, including different types of websites, identification of sensitive clients, and defeating website fingerprinting defenses.