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Found 8 papers, 0 papers with code
Requirement falsification for cyber-physical systems using generative models
We present the OGAN algorithm for automatic requirement falsification of cyber-physical systems.
WOGAN at the SBST 2022 CPS Tool Competition
WOGAN is an online test generation algorithm based on Wasserstein generative adversarial networks.
Wasserstein Generative Adversarial Networks for Online Test Generation for Cyber Physical Systems
We propose a novel online test generation algorithm WOGAN based on Wasserstein Generative Adversarial Networks.
Falsification of Multiple Requirements for Cyber-Physical Systems Using Online Generative Adversarial Networks and Multi-Armed Bandits
Our experiments indicate that, in addition to saving resources, this multi-armed bandit algorithm can falsify requirements with fewer number of executions on the system under test when compared to (i) an algorithm training a single GAN for the complete conjunctive requirement and (ii) an algorithm always training $n$ GANs at each step.
Consistency of UML class, object and statechart diagrams using ontology reasoners
We propose an automatic approach to analyze the consistency and satisfiability of Unified Modeling Language UML models containing multiple class, object and statechart diagrams using logic reasoners for the Web Ontology Language OWL 2.
Early Detection of Network Attacks Using Deep Learning
We have empirically evaluated our approach on the CICIDS2017 dataset.
Online GANs for Automatic Performance Testing
The objective of the proposed approach is to generate, for a given test budget, a test suite containing a high number of tests revealing performance defects.
On the Verification and Validation of AI Navigation Algorithms
To remedy this, we propose the use of a systematic scenario-based testing approach to validate navigation algorithms extensively.
