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20212025
most citedHoneyModels: Machine Learning Honeypots

6 citations · 17 across the 15 of their papers we have counts for

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13 papers · 1 filter

cs.CR2025

Efficient Storage Integrity in Adversarial Settings

Quinn Burke, Ryan Sheatsley, Yohan Beugin +4

Storage integrity is essential to systems and applications that use untrusted storage (e.g., public clouds, end-user devices). However, known methods for achieving storage integrit…

cs.CR2025★ 1 cited

Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning

Kyle Domico, Jean-Charles Noirot Ferrand, Ryan Sheatsley +3

Attacks on machine learning models have been extensively studied through stateless optimization. In this paper, we demonstrate how a reinforcement learning (RL) agent can learn a n…

cs.CR2025

Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs

Jean-Charles Noirot Ferrand, Yohan Beugin, Eric Pauley +2

Alignment in large language models (LLMs) is used to enforce guidelines such as safety. Yet, alignment fails in the face of jailbreak attacks that modify inputs to induce unsafe ou…

cs.CR2024

ParTEETor: A System for Partial Deployments of TEEs within Tor

Rachel King, Quinn Burke, Yohan Beugin +5

The Tor anonymity network allows users such as political activists and those under repressive governments to protect their privacy when communicating over the internet. At the same…

cs.CR2024

On Scalable Integrity Checking for Secure Cloud Disks

Quinn Burke, Ryan Sheatsley, Rachel King +3

Merkle hash trees are the standard method to protect the integrity and freshness of stored data. However, hash trees introduce additional compute and I/O costs on the I/O critical…

cs.CR2024

Characterizing the Modification Space of Signature IDS Rules

Ryan Guide, Eric Pauley, Yohan Beugin +2

Signature-based Intrusion Detection Systems (SIDSs) are traditionally used to detect malicious activity in networks. A notable example of such a system is Snort, which compares net…