Opacity is an important information-flow security property in the analysis of cyber-physical systems. It captures the plausible deniability of the system's secret behavior in the presence of an intruder that may access the information flow. Existing works on opacity only consider non-metric systems by assuming that the intruder can always distinguish two different outputs precisely. In this paper, we extend the concept of opacity to systems whose output sets are equipped with metrics. Such systems are widely used in the modeling of many real-world systems whose measurements are physical signals. A new concept called approximate opacity is proposed in order to quantitatively evaluate the security guarantee level with respect to the measurement precision of the intruder. Then we propose a new simulation-type relation, called approximate opacity preserving simulation relation, which characterizes how close two systems are in terms of the satisfaction of approximate opacity. This allows us to verify approximate opacity for large-scale, or even infinite systems, using their abstractions. We also discuss how to construct approximate opacity preserving symbolic models for a class of discrete-time control systems. Our results extend the definitions and analysis techniques for opacity from non-metric systems to metric systems.
Finally, approximate notions of two related properties called diagnosability and predictability are investigated recently in @cite_37 @cite_6 . Their setting is very similar to us as we both consider a measurement uncertainty threshold. However, diagnosability and predictability are properties, which can be preserved by standard approximate simulation relation. Our notions of opacity are and we show that standard approximate simulation relation does not preserve opacity. Therefore, the proposed approximate opacity preserving simulation relation is different from the standard approximate simulation relation in the literature.
@cite_37 @cite_6
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Abstract In this paper we introduce and characterize the notion of approximate predictability for the general class of metric systems, which are a powerful modeling framework to deal with complex heterogeneous systems such as hybrid systems. Approximate predictability corresponds to the possibility of predicting the occurrence of specific states belonging to a particular subset of interest, in advance with respect to their occurrence, on the basis of observations corrupted by measurement errors. We establish a relation between approximate predictability of a given metric system and approximate predictability of a metric system that approximately simulates the given one. This relation allows checking approximate predictability of a system with an infinite number of states, provided that one is able to construct a metric system with a finite number of states and inputs, approximating the original one in the sense of approximate simulation. The analysis of approximate predictability of Piecewise Affine (PWA) systems is carried out as an application of the proposed approach. In this paper, we propose several opacity-preserving (bi)simulation relations for general nondeterministic transition systems (NTS) in terms of initial-state opacity, current-state opacity, K-step opacity, and infinite-step opacity. We also show how one can leverage quotient construction to compute such relations. In addition, we use a two-way observer method to verify opacity of nondeterministic finite transition systems (NFTSs). As a result, although the verification of opacity for infinite NTSs is generally undecidable, if one can find such an opacity-preserving relation from an infinite NTS to an NFTS, the (lack of) opacity of the NTS can be easily verified over the NFTS which is decidable. The increasing complexity in nowadays engineered systems requires great attention to safety hazards and occurrence of faults, which must be readily detected to possibly restore nominal behavior of the system. The notion of diagnosability plays a key role in this regard, since it corresponds to the possibility of detecting within a finite delay if a fault, or in general a hazardous situation, did occur. In this letter, we introduce and characterize the notion of approximate diagnosability for the general class of metric systems, which are typically used in the research community working on hybrid systems to deal with complex heterogeneous processes in, e.g., cyber-physical systems. This notion captures the possibility of detecting faults on the basis of measurements corrupted by errors, always introduced by non-ideal sensors in a real environment. Relations are established between approximate diagnosability of a given metric system and approximate diagnosability of a system that approximately simulates the given one. Application of the proposed results to the analysis of approximate diagnosability of nonlinear systems is finally discussed. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. In this work, we decompose the prediction error for adversarial examples (robust error) as the sum of the natural (classification) error and boundary error, and provide a differentiable upper bound using the theory of classification-calibrated loss, which is shown to be the tightest possible upper bound uniform over all probability distributions and measurable predictors. Inspired by our theoretical analysis, we also design a new defense method, TRADES, to trade adversarial robustness off against accuracy. Our proposed algorithm performs well experimentally in real-world datasets. The methodology is the foundation of our entry to the NeurIPS 2018 Adversarial Vision Challenge in which we won the 1st place out of 2,000 submissions, surpassing the runner-up approach by @math in terms of mean @math perturbation distance.