Jean-Michel Picod
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Generalized Power Attacks against Crypto Hardware using Long-Range Deep Learning
Karel Král
Marina Zhang
Transactions on Cryptographic Hardware and Embedded Systems (TCHES), IACR (2024)
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To make cryptographic processors more resilient against side-channel attacks, engineers have developed various countermeasures. However, the effectiveness of these countermeasures is often uncertain, as it depends on the complex interplay between software and hardware. Assessing a countermeasure’s effectiveness using profiling techniques or machine learning so far requires significant expertise and effort to be adapted to new targets which makes those assessments expensive. We argue that including cost-effective automated attacks will help chip design teams to quickly evaluate their countermeasures during the development phase, paving the way to more secure chips.In this paper, we lay the foundations toward such automated system by proposing GPAM, the first deep-learning system for power side-channel analysis that generalizes across multiple cryptographic algorithms, implementations, and side-channel countermeasures without the need for manual tuning or trace preprocessing. We demonstrate GPAM’s capability by successfully attacking four hardened hardware-accelerated elliptic-curve digital-signature implementations. We showcase GPAM’s ability to generalize across multiple algorithms by attacking a protected AES implementation and achieving comparable performance to state-of-the-art attacks, but without manual trace curation and within a limited budget. We release our data and models as an open-source contribution to allow the community to independently replicate our results and build on them.
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Hybrid Post-Quantum Signatures in Hardware Security Keys
Diana Ghinea
Jennifer Pullman
Julien Cretin
Rafael Misoczki
Stefan Kölbl
Applied Cryptography and Network Security Workshop (2023)
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Recent advances in quantum computing are increasingly jeopardizing the security of cryptosystems currently in widespread use, such as RSA or elliptic-curve signatures. To address this threat, researchers and standardization institutes have accelerated the transition to quantum-resistant cryptosystems, collectively known as Post-Quantum Cryptography (PQC). These PQC schemes present new challenges due to their larger memory and computational footprints and their higher chance of latent vulnerabilities.
In this work, we address these challenges by introducing a scheme to upgrade the digital signatures used by security keys to PQC. We introduce a hybrid digital signature scheme based on two building blocks: a classically-secure scheme, ECDSA, and a post-quantum secure one, Dilithium.
Our hybrid scheme maintains the guarantees of each underlying building block even if the other one is broken, thus being resistant to classical and quantum attacks.
We experimentally show that our hybrid signature scheme can successfully execute on current security keys, even though secure PQC schemes are known to require substantial resources.
We publish an open-source implementation of our scheme at https://github.com/google/OpenSK/releases/tag/hybrid-pqc so that other researchers can reproduce our results on a nRF52840 development kit.
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Investigating Commercial Pay-Per-Install and the Distribution of Unwanted Software
Ryan Rasti
Cait Phillips
Marc-André (MAD) Decoste
Chris Sharp
Fabio Tirelo
Ali Tofigh
Marc-Antoine Courteau
Lucas Ballard
Robert Shield
Nav Jagpal
Niels Provos
Damon McCoy
Proceedings of the USENIX Security Symposium (2016)
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In this work, we explore the ecosystem of commercial pay-per-install (PPI) and the role it plays in the proliferation of unwanted software. Commercial PPI enables companies to bundle their applications with more popular software in return for a fee, effectively commoditizing access to user devices. We develop an analysis pipeline to track the business relationships underpinning four of the largest commercial PPI networks and classify the software families bundled. In turn, we measure their impact on end users and enumerate the distribution techniques involved. We find that unwanted ad injectors, browser settings hijackers, and cleanup utilities dominate the software families buying installs. Developers of these families pay $0.10--$1.50 per install---upfront costs that they recuperate by monetizing users without their consent or by charging exorbitant subscription fees. Based on Google Safe Browsing telemetry, we estimate that PPI networks drive over 60 million download attempts every week---nearly three times that of malware. While anti-virus and browsers have rolled out defenses to protect users from unwanted software, we find evidence that PPI networks actively interfere with or evade detection. Our results illustrate the deceptive practices of some commercial PPI operators that persist today.
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Cloak of Visibility: Detecting When Machines Browse a Different Web
Alexandros Kapravelos
Oxana Comanescu
Proceedings of the 37th IEEE Symposium on Security and Privacy (2016)
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The contentious battle between web services and miscreants involved in blackhat search engine optimization and malicious advertisements has driven the underground to develop increasingly sophisticated techniques that hide the true nature of malicious sites. These web cloaking techniques hinder the effectiveness of security crawlers and potentially expose Internet users to harmful content. In this work, we study the spectrum of blackhat cloaking techniques that target browser, network, or contextual cues to detect organic visitors. As a starting point, we investigate the capabilities of ten prominent cloaking services marketed within the underground. This includes a first look at multiple IP blacklists that contain over 50 million addresses tied to the top five search engines and tens of anti-virus and security crawlers. We use our findings to develop an anti-cloaking system that detects split-view content returned to two or more distinct browsing profiles with an accuracy of 95.5% and a false positive rate of 0.9% when tested on a labeled dataset of 94,946 URLs. We apply our system to an unlabeled set of 135,577 search and advertisement URLs keyed on high-risk terms (e.g., luxury products, weight loss supplements) to characterize the prevalence of threats in the wild and expose variations in cloaking techniques across traffic sources. Our study provides the first broad perspective of cloaking as it affects Google Search and Google Ads and underscores the minimum capabilities necessary of security crawlers to bypass the state of the art in mobile, rDNS, and IP cloaking.
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