Symbolic Monitoring Against Specifications Parametric in Time and Data

Waga, Masaki; André, Étienne; Hasuo, Ichiro

doi:10.1007/978-3-030-25540-4_30

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…Complex, quantitative extensions of automata were studied in the recent years: after timed pattern matching on timed regular expressions [UFAM14] 18]) and then for parametric timed pattern matching [WA19,WAH22b], with application to offline monitoring. Then, techniques for pattern matching were lifted to monitoring against complex specification making use of timing parameters and data parameters [WAH19].…”

Section: Related Workmentioning

confidence: 99%

Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Ghosh,

André

2024

Logical Methods in Computer Science

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Monitoring the correctness of distributed cyber-physical systems is essential. Detecting possible safety violations can be hard when some samples are uncertain or missing. We monitor here black-box cyber-physical system, with logs being uncertain both in the state and timestamp dimensions: that is, not only the logged value is known with some uncertainty, but the time at which the log was made is uncertain too. In addition, we make use of an over-approximated yet expressive model, given by a non-linear extension of dynamical systems. Given an offline log, our approach is able to monitor the log against safety specifications with a limited number of false alarms. As a second contribution, we show that our approach can be used online to minimize the number of sample triggers, with the aim at energetic efficiency. We apply our approach to three benchmarks, an anesthesia model, an adaptive cruise controller and an aircraft orbiting system.

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Section: Related Workmentioning

confidence: 99%

Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Ghosh,

André

2024

Logical Methods in Computer Science

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“…Years later, [6,9] considered identification of timing and magnitude parameters in a parametric version of STL logic called Parametric Signal Temporal Logic (PSTL), in the context of runtime verification. Recently [3,[23][24][25], with a motivation similar to ours, considered parametric timed pattern matching with respect to parametric timed automata.…”

Section: Introductionmentioning

confidence: 99%

Pattern Matching and Parameter Identification for Parametric Timed Regular Expressions

Mambakam

Asarin

Basset

et al. 2023

Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control

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Timed formalisms such as Timed Automata (TA), Signal Temporal Logic (STL) and Timed Regular expressions (TRE) have been previously applied as behaviour specifications for monitoring or runtime verification, in particular, under the form of pattern-matching, i.e. computing the set of all the segments of a given system run that satisfy the specification.In this work, timed regular expressions with parameters (for timing delays and for signal values) are considered. We define several classes of parametric expressions (based on Boolean or real-valued signals and discrete events), and tackle the problem of computing a parametric match-set, i.e. the parameter values and time segments of data that give a match for a given expression. We propose efficient data structures for representing match-sets (combining zones and polytopes), and devise pattern-matching algorithms. All these different types and algorithms are combined into a single implementation under a tool named parameTRE. We illustrate the approach on several examples, from electrocardiograms to driving patterns. CCS CONCEPTS• Theory of computation → Timed and hybrid models; • Computing methodologies → Algebraic algorithms.

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“…For readers interested in exploration on trade-off between the expressiveness and monitoring efficiency, we provide further references on monitoring with parameters [165,166]. Specifically, the parameters may include data quantification [167][168][169], timed pattern matching [170,171], or both data and time parameters [172].…”

Section: Related Workmentioning

confidence: 99%

Towards secure and robust stateful deep learning systems with model-based analysis

Du¹

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More and more we start to embrace the convenience and effectiveness of the rapidly advancing artificial intelligence (AI) technologies in our lives and different industries. Within this revolution, deep learning (DL), as one of the key innovation in AI, has made significant progress over the past decades. However, even the state-of-the-art DL systems are susceptible to minor adversarial perturbations, and suffer from quality, reliability and security problems, preventing the deployment of DL systems on safety-and security-critical applications. An early-stage assessment of DL systems is crucial in discovering defects and improving the overall product quality.Mature analysis processes and techniques have been established for traditional software, but it is highly non-trivial to directly apply them to DL systems. These challenges have motivated researchers to investigate testing, verification and adversarial sample detection of feed-forward neural networks, but little has been done on the recurrent neural network (RNN)-based stateful DL systems. In this thesis, we initiate the first major effort on the white-box RNN analysis using model-based approach to focus on the security and robustness properties and demonstrate the usefulness with applications on test case production, attack generation, and adversarial sample detection. To further protect the DL systems, we propose an efficient monitoring algorithm which could be potentially used to shield DL systems against adversarial samples at runtime, based on the RNN behaviors reflected by the abstract models.The first part of the thesis focuses on RNN model extraction and offline analysis on the security and robustness properties based on the models extracted.

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Symbolic Monitoring Against Specifications Parametric in Time and Data

Cited by 9 publications

References 35 publications

Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Offline and online energy-efficient monitoring of scattered uncertain logs using a bounding model

Pattern Matching and Parameter Identification for Parametric Timed Regular Expressions

Towards secure and robust stateful deep learning systems with model-based analysis

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