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Found 12 papers, 5 papers with code
SureFED: Robust Federated Learning via Uncertainty-Aware Inward and Outward Inspection
We theoretically prove the robustness of our algorithm against data and model poisoning attacks in a decentralized linear regression setting.
Runtime Stealthy Perception Attacks against DNN-based Adaptive Cruise Control Systems
Adaptive Cruise Control (ACC) is a widely used driver assistance technology for maintaining the desired speed and safe distance to the leading vehicle.
A Formal Analysis of Karn's Algorithm
We present a formal description of the algorithm, and study its properties.
Backdoor Attacks in Peer-to-Peer Federated Learning
Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets.
Network-Level Adversaries in Federated Learning
Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy.
Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents
We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP.
Automated Attacker Synthesis for Distributed Protocols
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.
Cryptography and Security Formal Languages and Automata Theory
Are Self-Driving Cars Secure? Evasion Attacks against Deep Neural Networks for Steering Angle Prediction
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars.
Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics.
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model.
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms.
