Fabian Kaczmarczyck
Software Engineer at Google
Security and Anti-Abuse Research Team
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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)
Preview abstract
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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Spotlight: Malware Lead Generation at Scale
Bernhard Grill
Jennifer Pullman
Cecilia M. Procopiuc
David Tao
Borbala Benko
Proceedings of Annual Computer Security Applications Conference (ACSAC) (2020)
Preview abstract
Malware is one of the key threats to online security today, with applications ranging from phishing mailers to ransomware andtrojans. Due to the sheer size and variety of the malware threat, it is impractical to combat it as a whole. Instead, governments and companies have instituted teams dedicated to identifying, prioritizing, and removing specific malware families that directly affect their population or business model. The identification and prioritization of the most disconcerting malware families (known as malware hunting) is a time-consuming activity, accounting for more than 20% of the work hours of a typical threat intelligence researcher, according to our survey. To save this precious resource and amplify the team’s impact on users’ online safety we present Spotlight, a large-scale malware lead-generation framework. Spotlight first sifts through a large malware data set to remove known malware families, based on first and third-party threat intelligence. It then clusters the remaining malware into potentially-undiscovered families, and prioritizes them for further investigation using a score based on their potential business impact.
We evaluate Spotlight on 67M malware samples, to show that it can produce top-priority clusters with over 99% purity (i.e., homogeneity), which is higher than simpler approaches and prior work. To showcase Spotlight’s effectiveness, we apply it to ad-fraud malware hunting on real-world data. Using Spotlight’s output, threat intelligence researchers were able to quickly identify three large botnets that perform ad fraud.
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