113 citations · 334 across the 20 of their papers we have counts for
21 papers
SLFS: a Flexible, Low-Cost Distributed File System Using Serverless Designs
Cheng Hao, Yang, Paola Alsharabaty +3
Large-scale distributed file systems must provision resources for peak demand, yet file access patterns fluctuate significantly, leaving substantial capacity idle during off-peak p…
DROP: Poison Dilution via Knowledge Distillation for Federated Learning
Georgios Syros, Anshuman Suri, Farinaz Koushanfar +2
Federated Learning is vulnerable to adversarial manipulation, where malicious clients can inject poisoned updates to influence the global model's behavior. While existing defense m…
Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation
Harsh Chaudhari, Giorgio Severi, John Abascal +6
Retrieval Augmented Generation (RAG) expands the capabilities of modern large language models (LLMs), by anchoring, adapting, and personalizing their responses to the most relevant…
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection
Nasimeh Heydaribeni, Ruisi Zhang, Tara Javidi +2
In this work, we introduce SureFED, a novel framework for byzantine robust federated learning. Unlike many existing defense methods that rely on statistically robust quantities, ma…
Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems
Xugui Zhou, Anqi Chen, Maxfield Kouzel +4
Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle. This paper evaluates the sec…
HoLA Robots: Mitigating Plan-Deviation Attacks in Multi-Robot Systems with Co-Observations and Horizon-Limiting Announcements
Kacper Wardega, Max von Hippel, Roberto Tron +2
Emerging multi-robot systems rely on cooperation between humans and robots, with robots following automatically generated motion plans to service application-level tasks. Given the…
Byzantine Resilience at Swarm Scale: A Decentralized Blocklist Protocol from Inter-robot Accusations
Kacper Wardega, Max von Hippel, Roberto Tron +2
The Weighted-Mean Subsequence Reduced (W-MSR) algorithm, the state-of-the-art method for Byzantine-resilient design of decentralized multi-robot systems, is based on discarding out…
Backdoor Attacks in Peer-to-Peer Federated Learning
Georgios Syros, Gokberk Yar, Simona Boboila +2
Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets. While federated learning (FL) mitigates to so…
Formal Model-Driven Analysis of Resilience of GossipSub to Attacks from Misbehaving Peers
Ankit Kumar, Max von Hippel, Pete Manolios +1
GossipSub is a new peer-to-peer communication protocol designed to counter attacks from misbehaving peers by controlling what information is sent and to whom, via a score function…
Network-Level Adversaries in Federated Learning
Giorgio Severi, Matthew Jagielski, Gökberk Yar +3
Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy. Prior work identified a range of security threats on fed…
A Flash(bot) in the Pan: Measuring Maximal Extractable Value in Private Pools
Ben Weintraub, Christof Ferreira Torres, Cristina Nita-Rotaru +1
The rise of Ethereum has lead to a flourishing decentralized marketplace that has, unfortunately, fallen victim to frontrunning and Maximal Extractable Value (MEV) activities, wher…
ShorTor: Improving Tor Network Latency via Multi-hop Overlay Routing
Kyle Hogan, Sacha Servan-Schreiber, Zachary Newman +3
We present ShorTor, a protocol for reducing latency on the Tor network. ShorTor uses multi-hop overlay routing, a technique typically employed by content delivery networks, to infl…
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
Maria Leonor Pacheco, Max von Hippel, Ben Weintraub +2
Automated attack discovery techniques, such as attacker synthesis or model-based fuzzing, provide powerful ways to ensure network protocols operate correctly and securely. Such tec…
SPON: Enabling Resilient Inter-Ledgers Payments with an Intrusion-Tolerant Overlay
Lucian Trestioreanu, Cristina Nita-Rotaru, Aanchal Malhotra +1
Payment systems are a critical component of everyday life in our society. While in many situations payments are still slow, opaque, siloed, expensive or even fail, users expect the…
Structural Attacks on Local Routing in Payment Channel Networks
Ben Weintraub, Cristina Nita-Rotaru, Stefanie Roos
Payment channel networks (PCN) enable scalable blockchain transactions without fundamentally changing the underlying distributed ledger algorithm. However, routing a payment via mu…
Automated Attacker Synthesis for Distributed Protocols
Max von Hippel, Cole Vick, Stavros Tripakis +1
Distributed protocols should be robust to both benign malfunction (e.g. packet loss or delay) and attacks (e.g. message replay) from internal or external adversaries. In this paper…
The House That Knows You: User Authentication Based on IoT Data
Talha Ongun, Oliver Spohngellert, Alina Oprea +3
Home-based Internet of Things (IoT) devices have gained in popularity and many households have become 'smart' by using devices such as smart sensors, locks, and voice-based assista…
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction
Alesia Chernikova, Alina Oprea, Cristina Nita-Rotaru +1
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars. However, the security of DNN models in this context leads to major safety impli…
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Samuel Jero, Maria Leonor Pacheco, Dan Goldwasser +1
Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar…
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor +5
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transfera…
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
Matthew Jagielski, Alina Oprea, Battista Biggio +3
As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms. In…