Mockingbird: Defending Against Deep-Learning-Based Website Fingerprinting Attacks with Adversarial Traces

Website Fingerprinting (WF) is a type of traffic analysis attack that enables a local passive eavesdropper to infer the victim's activity, even when the traffic is protected by a VPN or an anonymity system like Tor. Leveraging a deep-learning classifier, a WF attacker can gain over 98% accuracy on Tor traffic. In this paper, we explore a novel defense, Mockingbird, based on the idea of adversarial examples that have been shown to undermine machine-learning classifiers in other domains. Since the attacker gets to design and train his attack classifier based on the defense, we first demonstrate that at a straightforward technique for generating adversarial-example based traces fails to protect against an attacker using adversarial training for robust classification. We then propose Mockingbird, a technique for generating traces that resists adversarial training by moving randomly in the space of viable traces and not following more predictable gradients. The technique drops the accuracy of the state-of-the-art attack hardened with adversarial training from 98% to 42-58% while incurring only 58% bandwidth overhead. The attack accuracy is generally lower than state-of-the-art defenses, and much lower when considering Top-2 accuracy, while incurring lower bandwidth overheads.Comment: 18 pages, 13 figures and 8 Tables. Accepted in IEEE Transactions on Information Forensics and Security (TIFS

Measuring Information Leakage in Website Fingerprinting Attacks and Defenses

Tor provides low-latency anonymous and uncensored network access against a local or network adversary. Due to the design choice to minimize traffic overhead (and increase the pool of potential users) Tor allows some information about the client's connections to leak. Attacks using (features extracted from) this information to infer the website a user visits are called Website Fingerprinting (WF) attacks. We develop a methodology and tools to measure the amount of leaked information about a website. We apply this tool to a comprehensive set of features extracted from a large set of websites and WF defense mechanisms, allowing us to make more fine-grained observations about WF attacks and defenses.Comment: In Proceedings of the 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS '18

How Unique is Your .onion? An Analysis of the Fingerprintability of Tor Onion Services

Recent studies have shown that Tor onion (hidden) service websites are particularly vulnerable to website fingerprinting attacks due to their limited number and sensitive nature. In this work we present a multi-level feature analysis of onion site fingerprintability, considering three state-of-the-art website fingerprinting methods and 482 Tor onion services, making this the largest analysis of this kind completed on onion services to date. Prior studies typically report average performance results for a given website fingerprinting method or countermeasure. We investigate which sites are more or less vulnerable to fingerprinting and which features make them so. We find that there is a high variability in the rate at which sites are classified (and misclassified) by these attacks, implying that average performance figures may not be informative of the risks that website fingerprinting attacks pose to particular sites. We analyze the features exploited by the different website fingerprinting methods and discuss what makes onion service sites more or less easily identifiable, both in terms of their traffic traces as well as their webpage design. We study misclassifications to understand how onion service sites can be redesigned to be less vulnerable to website fingerprinting attacks. Our results also inform the design of website fingerprinting countermeasures and their evaluation considering disparate impact across sites.Comment: Accepted by ACM CCS 201

Automated Website Fingerprinting through Deep Learning

Several studies have shown that the network traffic that is generated by a visit to a website over Tor reveals information specific to the website through the timing and sizes of network packets. By capturing traffic traces between users and their Tor entry guard, a network eavesdropper can leverage this meta-data to reveal which website Tor users are visiting. The success of such attacks heavily depends on the particular set of traffic features that are used to construct the fingerprint. Typically, these features are manually engineered and, as such, any change introduced to the Tor network can render these carefully constructed features ineffective. In this paper, we show that an adversary can automate the feature engineering process, and thus automatically deanonymize Tor traffic by applying our novel method based on deep learning. We collect a dataset comprised of more than three million network traces, which is the largest dataset of web traffic ever used for website fingerprinting, and find that the performance achieved by our deep learning approaches is comparable to known methods which include various research efforts spanning over multiple years. The obtained success rate exceeds 96% for a closed world of 100 websites and 94% for our biggest closed world of 900 classes. In our open world evaluation, the most performant deep learning model is 2% more accurate than the state-of-the-art attack. Furthermore, we show that the implicit features automatically learned by our approach are far more resilient to dynamic changes of web content over time. We conclude that the ability to automatically construct the most relevant traffic features and perform accurate traffic recognition makes our deep learning based approach an efficient, flexible and robust technique for website fingerprinting.Comment: To appear in the 25th Symposium on Network and Distributed System Security (NDSS 2018

DEFENDING AGAINST DEEP LEARNING-BASED VIDEO FINGERPRINTING ATTACKS WITH ADVERSARIAL EXAMPLES

In an increasingly digital world, online anonymity and privacy is a paramount issue for internet users. Tools like The Onion Router (Tor) offer users anonymous internet browsing. Recently, however, Tor's anonymity has been compromised through fingerprinting, where machine learning models are used to analyze Tor traffic and predict user viewing habits, with some models achieving an accuracy of over 99%. There are defenses for Tor that attempt to prevent fingerprinting, but many are defeated by new techniques that utilize Deep Neural Networks (DNNs). New defenses that are robust against DNNs use adversarial examples to fool the classifier, but those defenses either assume the user has access to the full traffic trace beforehand or require expensive maintenance from Tor servers. In this thesis, we propose Prism, a defense against fingerprinting attacks that uses adversarial examples to fool classifiers in real time. We describe a novel method of adversarial example generation that enables adversarial example creation as input is learned over time. Prism injects these adversarial examples into the Tor traffic stream to prevent DNNs from accurately predicting sites that a user is viewing, even if the DNN is hardened by adversarial training. We show that Prism reduces the accuracy of defended fingerprinting models from over 98% to 0%. We also show that Prism can be implemented entirely on the server side, increasing deployability for users who run Tor on devices without GPUs.Outstanding ThesisEnsign, United States NavyApproved for public release. Distribution is unlimited