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
more »
« less
Analyzing Cache Side Channels Using Deep Neural Networks
Cache side-channel attacks aim to breach the confidentiality of a computer system and extract sensitive secrets through CPU caches. In the past years, different types of side-channel attacks targeting a variety of cache architectures have been demonstrated. Meanwhile, different defense methods and systems have also been designed to mitigate these attacks. However, quantitatively evaluating the effectiveness of these attacks and defenses has been challenging. We propose a generic approach to evaluating cache side-channel attacks and defenses. Specifically, our method builds a deep neural network with its inputs as the adversary's observed information, and its outputs as the victim's execution traces. By training the neural network, the relationship between the inputs and outputs can be automatically discovered. As a result, the prediction accuracy of the neural network can serve as a metric to quantify how much information the adversary can obtain correctly, and how effective a defense solution is in reducing the information leakage under different attack scenarios. Our evaluation suggests that the proposed method can effectively evaluate different attacks and defenses.
more »
« less
- Award ID(s):
- 1814190
- NSF-PAR ID:
- 10207982
- Date Published:
- Journal Name:
- 34th Annual Computer Security Applications Conference (ACSAC)
- Page Range / eLocation ID:
- 174 to 186
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Typical cybersecurity solutions emphasize on achieving defense functionalities. However, execution efficiency and scalability are equally important, especially for real-world deployment. Straightforward mappings of cybersecurity applications onto HPC platforms may significantly underutilize the HPC devices’ capacities. On the other hand, the sophisticated implementations are quite difficult: they require both in-depth understandings of cybersecurity domain-specific characteristics and HPC architecture and system model. In our work, we investigate three sub-areas in cybersecurity, including mobile software security, network security, and system security. They have the following performance issues, respectively: 1) The flow- and context-sensitive static analysis for the large and complex Android APKs are incredibly time-consuming. Existing CPU-only frameworks/tools have to set a timeout threshold to cease the program analysis to trade the precision for performance. 2) Network intrusion detection systems (NIDS) use automata processing as its searching core and requires line-speed processing. However, achieving high-speed automata processing is exceptionally difficult in both algorithm and implementation aspects. 3) It is unclear how the cache configurations impact time-driven cache side-channel attacks’ performance. This question remains open because it is difficult to conduct comparative measurement to study the impacts. In this dissertation, we demonstrate how application-specific characteristics can be leveraged to optimize implementations on various types of HPC for faster and more scalable cybersecurity executions. For example, we present a new GPU-assisted framework and a collection of optimization strategies for fast Android static data-flow analysis that achieve up to 128X speedups against the plain GPU implementation. For network intrusion detection systems (IDS), we design and implement an algorithm capable of eliminating the state explosion in out-of-order packet situations, which reduces up to 400X of the memory overhead. We also present tools for improving the usability of Micron’s Automata Processor. To study the cache configurations’ impact on time-driven cache side-channel attacks’ performance, we design an approach to conducting comparative measurement. We propose a quantifiable success rate metric to measure the performance of time-driven cache attacks and utilize the GEM5 platform to emulate the configurable cache.more » « less
-
The Least-Recently Used cache replacement policy and its variants are widely deployed in modern processors. This paper shows for the first time in detail that the LRU states of caches can be used to leak information: any access to a cache by a sender will modify the LRU state, and the receiver is able to observe this through a timing measurement. This paper presents LRU timing-based channels both when the sender and the receiver have shared memory, e.g., shared library data pages, and when they are separate processes without shared memory. In addition, the new LRU timing-based channels are demonstrated on both Intel and AMD processors in scenarios where the sender and the receiver are sharing the cache in both hyper-threaded setting and time-sliced setting. The transmission rate of the LRU channels can be up to 600Kbpsper cache set in the hyper-threaded setting. Different from the majority of existing cache channels which require the sender to trigger cache misses, the new LRU channels work with the sender only having cache hits, making the channel faster and more stealthy. This paper also demonstrates that the new LRU channels can be used in transient execution attacks, e.g., Spectre. Further, this paper shows that the LRU channels pose threats to existing secure cache designs, and this work demonstrates the LRU channels affect the secure PL cache. The paper finishes by discussing and evaluating possible defenses.more » « less
-
Abstract—Recent work has demonstrated the security risk associated with micro-architecture side-channels. The cache timing side-channel is a particularly popular target due to its availability and high leakage bandwidth. Existing proposals for defending cache side-channel attacks either degrade cache performance and/or limit cache sharing, hence, should only be invoked when the system is under attack. A lightweight monitoring mechanism that detects malicious micro-architecture manipulation in realistic environments is essential for the judicious deployment of these defense mechanisms. In this paper, we propose PREDATOR, a cache side-channel attack detector that identifies cache events caused by an attacker. To detect side-channel attacks in noisy environments, we take advantage of the observation that, unlike non-specific noises, an active attacker alters victim’s micro-architectural states on security critical accesses and thus causes the victim extra cache events on those accesses. PREDATOR uses precise performance counters to collect detailed victim’s access information and analyzes location-based deviations. PREDATOR is capable of detecting five different attacks with high accuracy and limited performance overhead in complex noisy execution environments. PREDATOR remains effective even when the attacker slows the attack rate by 256 times. Furthermore, PREDATOR is able to accurately report details about the attack such as the instruction that accesses the attacked data. In the case of GnuPG RSA [20], PREDATOR can pinpoint the square/multiply operations in the Modulo-Reduce algorithm; and in the case of OpenSSL AES [45], it can identify the accesses to the Te-Table.more » « less
-
Spectre and Meltdown attacks and their variants exploit hardware performance optimization features to cause security breaches. Secret information is accessed and leaked through covert or side channels. New attack variants keep appearing and we do not have a systematic way to capture the critical characteristics of these attacks and evaluate why they succeed or fail.In this paper, we provide a new attack-graph model for reasoning about speculative execution attacks. We model attacks as ordered dependency graphs, and prove that a race condition between two nodes can occur if there is a missing dependency edge between them. We define a new concept, “security dependency”, between a resource access and its prior authorization operation. We show that a missing security dependency is equivalent to a race condition between authorization and access, which is a root cause of speculative execution attacks. We show detailed examples of how our attack graph models the Spectre and Meltdown attacks, and is generalizable to all the attack variants published so far. This attack model is also very useful for identifying new attacks and for generalizing defense strategies. We identify several defense strategies with different performance-security tradeoffs. We show that the defenses proposed so far all fit under one of our defense strategies. We also explain how attack graphs can be constructed and point to this as promising future work for tool designersmore » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3274694.3274715
