Search Results for author:
Found 9 papers, 6 papers with code
Compositional Inductive Invariant Based Verification of Neural Network Controlled Systems
Verifying the inductiveness of a candidate inductive invariant in the context of NNCS is hard because of the scale and nonlinearity of neural networks.
Safe Environmental Envelopes of Discrete Systems
This paper proposes a notion of \emph{robustness} as an explicit, first-class property of a transition system that captures how robust it is against possible \emph{deviations} in the environment.
On Neural Network Equivalence Checking using SMT Solvers
Two pretrained neural networks are deemed equivalent if they yield similar outputs for the same inputs.
On tolerance of discrete systems with respect to transition perturbations
In this paper, we propose an approach for analyzing control systems with respect to their tolerance against environmental perturbations.
Adversarial Robustness Verification and Attack Synthesis in Stochastic Systems
We outline a class of threat models under which adversaries can perturb system transitions, constrained by an $\varepsilon$ ball around the original transition probabilities.
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
Metrics and methods for robustness evaluation of neural networks with generative models
In this paper, we propose several metrics to measure robustness of classifiers to natural adversarial examples, and methods to evaluate them.
Automated Synthesis of Secure Platform Mappings
In this paper, we introduce the problem of synthesizing a property-preserving platform mapping: A set of implementation decisions ensuring that a desired property is preserved from a high-level design into a low-level platform implementation.
Software Engineering
Learning Moore Machines from Input-Output Traces
We develop three algorithms for solving this problem: (1) the PTAP algorithm, which transforms a set of input-output traces into an incomplete Moore machine and then completes the machine with self-loops; (2) the PRPNI algorithm, which uses the well-known RPNI algorithm for automata learning to learn a product of automata encoding a Moore machine; and (3) the MooreMI algorithm, which directly learns a Moore machine using PTAP extended with state merging.
