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Abstract Enforcement of information-flow policies has been extensively studied by language-based approaches over the past few decades. In this paper, we propose an alternative, novel, general, and effective approach using enforcement ofhyperproperties– a powerful formalism for expressing and reasoning about a wide range of information-flow security policies. We studyblack-vs.gray-vs.white-boxenforcement of hyperproperties expressed by nondeterministic finite-word hyperautomata (NFH), where the enforcer has null, some, or complete information about the implementation of the system under scrutiny. Given an NFH, in order to generate a runtime enforcer, we reduce the problem to controller synthesis for hyperproperties and subsequently to the satisfiability problem for quantified Boolean formulas (QBFs). The resulting enforcers are transferable with low-overhead. We conduct a rich set of case studies, including information-flow control for JavaScript code, as well as synthesizing obfuscators for control plants. 

Predictive monitoring—making predictions about future states and monitoring if the predicted states satisfy requirements—offers a promising paradigm in supporting the decision making of Cyber-Physical Systems (CPS). Existing works of predictive monitoring mostly focus on monitoring individual predictions rather than sequential predictions. We develop a novel approach for monitoring sequential predictions generated from Bayesian Recurrent Neural Networks (RNNs) that can capture the inherent uncertainty in CPS, drawing on insights from our study of real-world CPS datasets. We propose a new logic named Signal Temporal Logic with Uncertainty (STL-U) to monitor a flowpipe containing an infinite set of uncertain sequences predicted by Bayesian RNNs. We define STL-U strong and weak satisfaction semantics based on whether all or some sequences contained in a flowpipe satisfy the requirement. We also develop methods to compute the range of confidence levels under which a flowpipe is guaranteed to strongly (weakly) satisfy an STL-U formula. Furthermore, we develop novel criteria that leverage STL-U monitoring results to calibrate the uncertainty estimation in Bayesian RNNs. Finally, we evaluate the proposed approach via experiments with real-world CPS datasets and a simulated smart city case study, which show very encouraging results of STL-U based predictive monitoring approach outperforming baselines. 

Shape expressions (SEs) is a novel specification language that was recently introduced to express behavioral patterns over real-valued signals observed during the execution of cyber-physical systems. An SE is a regular expression composed of arbitrary parameterized shapes, such as lines, exponential curves, and sinusoids as atomic symbols with symbolic constraints on the shape parameters. SEs enable a natural and intuitive specification of complex temporal patterns over possibly noisy data. In this article, we propose a novel method for mining a broad and interesting fragment of SEs from time-series data using a combination of techniques from linear regression, unsupervised clustering, and learning finite automata from positive examples. The learned SE for a given dataset provides an explainable and intuitive model of the observed system behavior. We demonstrate the applicability of our approach on two case studies from different application domains and experimentally evaluate the implemented specification mining procedure. 

Evaluation of scientific contributions can be done in many different ways. For the various research communities working on the verification of systems (software, hardware, or the underlying involved mechanisms), it is important to bring together the community and to compare the state of the art, in order to identify progress of and new challenges in the research area. Competitions are a suitable way to do that.