Neural Networks for Runtime Verification

Alan Perotti, Artur S. d'Avila Garcez, Guido Boella

Abstract: A recent trend in High-Performance Computation is parallel computing, and the field of Neural Networks is showing impressive improvements in performance, especially with the use of GPU accelerators. In this paper, we use neural networks to improve the performance of Runtime Verification. Runtime verification is used in a variety of domains -from policy enforcement to electronic fraud detection-to automatically check whether a system meets a temporal specification, by observing the output of the system. In this paper, we present a novel run-time monitoring system, RuleRunner, and we exploit results from the Neural-Symbolic Integration area to encode it in a recurrent neural network. The results show that neural networks can perform real-time online runtime verification. Performance was improved by the parallel architecture and the matrix-based implementation with GPU.