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 sidechannel 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 lastlevel cache. We show that our approach achieves high classification accuracy in both the open-world and the closedworld 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.

Abstract. Measurement errors make power analysis attacks difficult to mount when only a single power trace is available: the statistical methods that make DPA attacks so successful are not applicable since they require many (typically thousands) of traces. Recently it was suggested by [18] to use algebraic methods for the single-trace scenario, converting the key recovery problem into a Boolean satisfiability (SAT) problem, then using a SAT solver. However, this approach is extremely sensitive to noise (allowing an error rate of well under 1% at most), and the question of its practicality remained open. In this work we show how a single-trace side-channel analysis problem can be transformed into a pseudo-Boolean optimization (PBOPT) problem, which takes errors into consideration. The PBOPT instance can then be solved using a suitable optimization problem solver. The PBOPT syntax provides for a more expressive input specification which allows a very natural representation of measurement errors. Most importantly, we show that using our approach we are able to mount successful and efficient single-trace attacks even in the presence of realistic error rates of 10%-20%. We call our new attack methodology Tolerant Algebraic Side-Channel Analysis (TASCA). We show practical attacks on two real ciphers: Keeloq and AES.

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In the attempt to bring modern broadband Internet features to traditional broadcast television, the Digital Video Broadcasting (DVB) consortium introduced a specification called Hybrid Broadcast-Broadband Television (HbbTV), which allows broadcast streams to include embedded HTML content that is rendered by the television. This system is already in very wide deployment in Europe and has recently been adopted as part of the American digital television standard. Our analyses of the specifications, and of real systems implementing them, show that the broadband and broadcast systems are combined insecurely. This enables a large-scale exploitation technique with a localized geographical footprint based on Radio Frequency (RF) injection, which requires a minimal budget and infrastructure and is remarkably difficult to detect. In this article, we present the attack methodology and a number of follow-on exploitation techniques that provide significant flexibility to attackers. Furthermore, we demonstrate that the technical complexity and required budget are low, making this attack practical and realistic, especially in areas with high population density: In a dense urban area, an attacker with a budget of about $450 can target more than 20,000 devices in a single attack. A unique aspect of this attack is that, in contrast to most Internet of Things/Cyber-Physical System threat scenarios, where the attack comes from the data network side and affects the physical world, our attack uses the physical broadcast network to attack the data network.

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