We analyze the robustness of the known variational Bayesian peer-to-peer federated learning framework (BayP2PFL) against poisoning attacks and subsequently show that BayP2PFL is not robust against those attacks.
Adaptive Cruise Control (ACC) is a widely used driver assistance feature for maintaining desired speed and safe distance to the leading vehicles.
Most machine learning applications rely on centralized learning processes, opening up the risk of exposure of their training datasets.
Federated learning is a popular strategy for training models on distributed, sensitive data, while preserving data privacy.
We show the generalizability of our FSM extraction by using the RFCs for six different protocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP.
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
Deep Neural Networks (DNNs) have tremendous potential in advancing the vision for self-driving cars.
Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics.
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model.
As machine learning becomes widely used for automated decisions, attackers have strong incentives to manipulate the results and models generated by machine learning algorithms.