Website fingerprinting attacks, which use statistical analysis on network traffic to compromise user privacy, have been shown to be effective even if the traffic is sent over anonymity-preserving networks such as Tor. The classical attack model used to evaluate website fingerprinting attacks assumes an on-path adversary, who can observe all traffic
traveling between the user’s computer and the secure network.
In this work we investigate these attacks under a different attack model, in which the adversary is capable of sending a small amount of malicious JavaScript code to the target
user’s computer. The malicious code mounts a cache side-channel attack, which exploits the effects of contention on the CPU’s cache, to identify other websites being browsed.
The effectiveness of this attack scenario has never been systematically analyzed, especially in the open-world model which assumes that the user is visiting a mix of both sensitive
and non-sensitive sites.
We show that cache website fingerprinting attacks in JavaScript are highly feasible. Specifically, we use machine learning techniques to classify traces of cache activity.
Unlike prior works, which try to identify cache conflicts, our work measures the overall occupancy of the last-level cache. We show that our approach achieves high classification
accuracy in both the open-world and the closed-world models. We further show that our attack is more resistant than network-based fingerprinting to the effects of response
caching, and that our techniques are resilient both to network-based defenses and to side-channel countermeasures introduced to modern browsers as a response to the
Spectre attack. To protect against cache-based website fingerprinting, new defense mechanisms must be introduced to privacy-sensitive browsers and websites. We investigate one such mechanism, and show that generating artificial cache activity reduces the effectiveness of the attack and completely eliminates it when used in the Tor Browser
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Guard Placement Attacks on Path Selection Algorithms for Tor
Abstract The popularity of Tor has made it an attractive target for a variety of deanonymization and fingerprinting attacks. Location-based path selection algorithms have been proposed as a countermeasure to defend against such attacks. However, adversaries can exploit the location-awareness of these algorithms by strategically placing relays in locations that increase their chances of being selected as a client’s guard. Being chosen as a guard facilitates website fingerprinting and traffic correlation attacks over extended time periods. In this work, we rigorously define and analyze the guard placement attack . We present novel guard placement attacks and show that three state-of-the-art path selection algorithms—Counter-RAPTOR, DeNASA, and LASTor—are vulnerable to these attacks, overcoming defenses considered by all three systems. For instance, in one attack, we show that an adversary contributing only 0.216% of Tor’s total bandwidth can attain an average selection probability of 18.22%, 84× higher than what it would be under Tor currently. Our findings indicate that existing location-based path selection algorithms allow guards to achieve disproportionately high selection probabilities relative to the cost required to run the guard. Finally, we propose and evaluate a generic defense mechanism that provably defends any path selection algorithm against guard placement attacks. We run our defense mechanism on each of the three path selection algorithms, and find that our mechanism significantly enhances the security of these algorithms against guard placement attacks with only minimal impact to the goals or performance of the original algorithms.
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- Award ID(s):
- 1704105
- NSF-PAR ID:
- 10107793
- Date Published:
- Journal Name:
- Proceedings on Privacy Enhancing Technologies
- Volume:
- 2019
- Issue:
- 4
- ISSN:
- 2299-0984
- Page Range / eLocation ID:
- 272 to 291
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Recent work has shown that Tor is vulnerable to attacks that manipulate inter-domain routing to compromise user privacy. Proposed solutions such as Counter-RAPTOR [29] attempt to ameliorate this issue by favoring Tor entry relays that have high resilience to these attacks. However, because these defenses bias Tor path selection on the identity of the client, they invariably leak probabilistic information about client identities. In this work, we make the following contributions. First, we identify a novel means to quantify privacy leakage in guard selection algorithms using the metric of Max-Divergence. Max-Divergence ensures that probabilistic privacy loss is within strict bounds while also providing composability over time. Second, we utilize Max-Divergence and multiple notions of entropy to understand privacy loss in the worst-case for Counter-RAPTOR. Our worst-case analysis provides a fresh perspective to the field, as prior work such as Counter-RAPTOR only analyzed average case-privacy loss. Third, we propose modifications to Counter-RAPTOR that incorporate worst-case Max-Divergence in its design. Specifically, we utilize the exponential mechanism (a mechanism for differential privacy) to guarantee a worst-case bound on Max-Divergence/privacy loss. For the quality function used in the exponential mechanism, we show that a Monte-Carlo sampling-based method for stochastic optimization can be used to improve multi-dimensional trade-offs between security, privacy, and performance. Finally, we demonstrate that compared to Counter-RAPTOR, our approach achieves an 83% decrease in Max-Divergence after one guard selection and a 245% increase in worst-case Shannon entropy after 5 guard selections. Notably, experimental evaluations using the Shadow emulator shows that our approach provides these privacy benefits with minimal impact on system performance.more » « less
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Website Fingerprinting (WF) is a traffic analysis attack that enables an eavesdropper to infer the victim's web activity even when encrypted and even when using the Tor anonymity system. Using deep learning classifiers, the attack can reach up to 98% accuracy. Existing WF defenses are either too expensive in terms of bandwidth and latency overheads (e.g. 2-3 times as large or slow) or ineffective against the latest attacks. In this work, we explore a novel defense based on the idea of adversarial examples that have been shown to undermine machine learning classifiers in other domains. Our Adversarial Traces defense adds padding to a Tor traffic trace in a manner that reliably fools the classifier into classifying it as coming from a different site. The technique drops the accuracy of the state-of-the-art attack from 98% to 60%, while incurring a reasonable 47% bandwidth overhead, showing its promise as a possible defense for Tor.more » « less
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Abstract Website Fingerprinting (WF) attacks are used by local passive attackers to determine the destination of encrypted internet traffic by comparing the sequences of packets sent to and received by the user to a previously recorded data set. As a result, WF attacks are of particular concern to privacy-enhancing technologies such as Tor. In response, a variety of WF defenses have been developed, though they tend to incur high bandwidth and latency overhead or require additional infrastructure, thus making them difficult to implement in practice. Some lighter-weight defenses have been presented as well; still, they attain only moderate effectiveness against recently published WF attacks. In this paper, we aim to present a realistic and novel defense, RegulaTor, which takes advantage of common patterns in web browsing traffic to reduce both defense overhead and the accuracy of current WF attacks. In the closed-world setting, RegulaTor reduces the accuracy of the state-of-the-art attack, Tik-Tok, against comparable defenses from 66% to 25.4%. To achieve this performance, it requires 6.6% latency overhead and a bandwidth overhead 39.3% less than the leading moderate-overhead defense. In the open-world setting, RegulaTor limits a precision-tuned Tik-Tok attack to an F 1 -score of. 135, compared to .625 for the best comparable defense.more » « less
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null (Ed.)Abstract A passive local eavesdropper can leverage Website Fingerprinting (WF) to deanonymize the web browsing activity of Tor users. The value of timing information to WF has often been discounted in recent works due to the volatility of low-level timing information. In this paper, we more carefully examine the extent to which packet timing can be used to facilitate WF attacks. We first propose a new set of timing-related features based on burst-level characteristics to further identify more ways that timing patterns could be used by classifiers to identify sites. Then we evaluate the effectiveness of both raw timing and directional timing which is a combination of raw timing and direction in a deep-learning-based WF attack. Our closed-world evaluation shows that directional timing performs best in most of the settings we explored, achieving: (i) 98.4% in undefended Tor traffic; (ii) 93.5% on WTF-PAD traffic, several points higher than when only directional information is used; and (iii) 64.7% against onion sites, 12% higher than using only direction. Further evaluations in the open-world setting show small increases in both precision (+2%) and recall (+6%) with directional-timing on WTF-PAD traffic. To further investigate the value of timing information, we perform an information leakage analysis on our proposed handcrafted features. Our results show that while timing features leak less information than directional features, the information contained in each feature is mutually exclusive to one another and can thus improve the robustness of a classifier.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2478/popets-2019-0069
