The Density of Linear-Time Properties

Finkbeiner, Bernd; Torfah, Hazem

doi:10.1007/978-3-319-68167-2_10

Cited by 5 publications

(3 citation statements)

References 23 publications

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“…Another approach is to enrich temporal logics with quantitative measures such as taking either the edit distance [33], counting the number of possible infinite models for LTL [27,52], incorporating aggregation expressions into metric first-order temporal logic [10], or using averaging temporal operators that quantify the degree of satisfaction of a signal for a specification by integrating the signal w.r.t. a constant reference signal [3].…”

Section: Related Workmentioning

confidence: 99%

From LTL to rLTL monitoring

Mascle

Neider

Schwenger

et al. 2020

Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control

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Runtime monitoring is commonly used to detect the violation of desired properties in safety critical cyber-physical systems by observing its executions. Bauer et al. introduced an influential framework for monitoring Linear Temporal Logic (LTL) properties based on a three-valued semantics: the formula is already satisfied by the given prefix, it is already violated, or it is still undetermined, i.e., it can still be satisfied and violated by appropriate extensions. However, a wide range of formulas are not monitorable under this approach, meaning that they have a prefix for which satisfaction and violation will always remain undetermined no matter how it is extended. In particular, Bauer et al. report that 44% of the formulas they consider in their experiments fall into this category. Recently, a robust semantics for LTL was introduced to capture different degrees by which a property can be violated. In this paper we introduce a robust semantics for finite strings and show its potential in monitoring: every formula considered by Bauer et al. is monitorable under our approach. Furthermore, we discuss which properties that come naturally in LTL monitoring -such as the realizability of all truth values -can be transferred to the robust setting. Lastly, we show that LTL formulas with robust semantics can be monitored by deterministic automata and report on a prototype implementation.

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Section: Related Workmentioning

confidence: 99%

From LTL to rLTL monitoring

Mascle

Neider

Schwenger

et al. 2020

Proceedings of the 23rd International Conference on Hybrid Systems: Computation and Control

View full text Add to dashboard Cite

show abstract

“…State-of-the-art tools for propositional model counting are Relsat [33] and c2d [23]. Algorithms for counting models of temporal logics and automata over infinite words have been introduced in [27,28,44]. The counting of projected models, i.e., when some parts of the models are irrelevant, was studied in [2], for which tools such as #CLASP [2] and DSharp P [2,41] exist.…”

Section: Related Workmentioning

confidence: 99%

Model Checking Quantitative Hyperproperties

Finkbeiner¹,

Hahn²,

Torfah³

2019

Preprint

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Hyperproperties are properties of sets of computation traces. In this paper, we study quantitative hyperproperties, which we define as hyperproperties that express a bound on the number of traces that may appear in a certain relation. For example, quantitative non-interference limits the amount of information about certain secret inputs that is leaked through the observable outputs of a system. Quantitative noninterference thus bounds the number of traces that have the same observable input but different observable output. We study quantitative hyperproperties in the setting of HyperLTL, a temporal logic for hyperproperties. We show that, while quantitative hyperproperties can be expressed in HyperLTL, the running time of the HyperLTL model checking algorithm is, depending on the type of property, exponential or even doubly exponential in the quantitative bound. We improve this complexity with a new model checking algorithm based on model-counting. The new algorithm needs only logarithmic space in the bound and therefore improves, depending on the property, exponentially or even doubly exponentially over the model checking algorithm of HyperLTL. In the worst case, the new algorithm needs polynomial space in the size of the system. Our Max#Sat-based prototype implementation demonstrates, however, that the counting approach is viable on systems with nontrivial quantitative information flow requirements such as a passcode checker.

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Section: Related Workmentioning

confidence: 99%

Model Checking Quantitative Hyperproperties

Finkbeiner

Hahn

Torfah

2018

Computer Aided Verification

Self Cite

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Abstract. Hyperproperties are properties of sets of computation traces. In this paper, we study quantitative hyperproperties, which we define as hyperproperties that express a bound on the number of traces that may appear in a certain relation. For example, quantitative non-interference limits the amount of information about certain secret inputs that is leaked through the observable outputs of a system. Quantitative noninterference thus bounds the number of traces that have the same observable input but different observable output. We study quantitative hyperproperties in the setting of HyperLTL, a temporal logic for hyperproperties. We show that, while quantitative hyperproperties can be expressed in HyperLTL, the running time of the HyperLTL model checking algorithm is, depending on the type of property, exponential or even doubly exponential in the quantitative bound. We improve this complexity with a new model checking algorithm based on model-counting. The new algorithm needs only logarithmic space in the bound and therefore improves, depending on the property, exponentially or even doubly exponentially over the model checking algorithm of HyperLTL. In the worst case, the new algorithm needs polynomial space in the size of the system. Our Max#Sat-based prototype implementation demonstrates, however, that the counting approach is viable on systems with nontrivial quantitative information flow requirements such as a passcode checker.

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The Density of Linear-Time Properties

Cited by 5 publications

References 23 publications

From LTL to rLTL monitoring

From LTL to rLTL monitoring

Model Checking Quantitative Hyperproperties

Model Checking Quantitative Hyperproperties

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