Learning Linear Temporal Properties

Neider, Daniel; Gavran, Ivan

doi:10.23919/fmcad.2018.8603016

Cited by 87 publications

(118 citation statements)

References 33 publications

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“…We do not distinguish these names, sometimes we use them interchangeably, and here we just use the term model learning or learning model instead. Similarly, the learning specification problems also have different names under research, typically are specification mining [38]- [43], specification inference [44], requirements mining [45], mining properties [46], learning logic formulae [47], learning specifications [48], [49], learning properties [50], [51], etc. Here we use the term learning specification (or specification learning) in analogy with learning model (or model learning).…”

Section: Taxonomy Of Learning Algorithms In Formal Methodsmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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Formal methods play an important role in testing and verifying software quality, especially in modern society with rapid technological updates. Learning-based techniques have been extensively applied to learn (a model or model-free) for formal verification and to learn system specifications, and resulted in numerous contributions. Due to the fact that adequate system models are often difficult to design manually and manual definition of specifications for such software systems gets infeasible, which motivate new research directions in learning models and/or specifications from observed system behaviors automatically. This paper mainly concentrates on learning-based techniques in formal methods area. An up-to-date overview of the current state-of-the-art in learning-based formal methods is provided in the paper. This paper is not a comprehensive survey of learning-based techniques in formal methods area, but rather as a survey of the taxonomy, applications and possible future directions in learning-based formal methods.

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Section: Taxonomy Of Learning Algorithms In Formal Methodsmentioning

confidence: 99%

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

et al. 2020

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“…A ¬ψ,v ) if ψ is syntactically co-safe (resp. syntactically safe) 8 for k = 0 to K do 9 Initialize p ψ,v L (L, q k ) 10 for = L to 2, j = 0 to K do 11 For each k ∈ [0, K], calculate c j,k p ψ,v…”

Section: Computation Of Information Gain Of Gtl Formulasmentioning

confidence: 99%

“…Our approach of inferring GTL formulas from data is closely related to inferring temporal logic formulas from data. The work in [2,5,8] focus on inferring temporal logic formulas for classifying two sets of trajectories, while the work in [3,6,9,15] focus on identifying temporal logic formulas from system trajectories.…”

Section: Introductionmentioning

confidence: 99%

Graph Temporal Logic Inference for Classification and Identification

Nettekoven

Julius

et al. 2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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Inferring spatial-temporal properties from data is important for many complex systems, such as additive manufacturing systems, swarm robotic systems and biological networks. Such systems can often be modeled as a labeled graph where labels on the nodes and edges represent relevant measurements such as temperatures and distances. We introduce graph temporal logic (GTL) which can express properties such as "whenever a node's label is above 10, for the next 3 time units there are always at least two neighboring nodes with an edge label of at most 2 where the node labels are above 5". This paper is a first attempt to infer spatial (graph) temporal logic formulas from data for classification and identification. For classification, we infer a GTL formula that classifies two sets of graph temporal trajectories with minimal misclassification rate. For identification, we infer a GTL formula that is informative and is satisfied by the graph temporal trajectories in the dataset with high probability. The informativeness of a GTL formula is measured by the information gain with respect to given prior knowledge represented by a prior probability distribution. We implement the proposed approach to classify the graph patterns of tensile specimens built from selective laser sintering (SLS) process with varying strengths, and to identify informative spatialtemporal patterns from experimental data of the SLS cooldown process and simulation data of a swarm of robots.

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“…We show that for all the above representations the automatic signature synthesis problem can be viewed as an instance of the language learning from the informant problem. For DFA and MM representations, we rely on existing automata learning algorithms, whereas for PLTL, we propose a new algorithm, an extension of prior work [43]. For runtime monitoring of these signature representations in PHOENIX, we use standard algorithms [27].…”

Section: Introductionmentioning

confidence: 99%

PHOENIX: Device-Centric Cellular Network Protocol Monitoring using Runtime Verification

Echeverria¹,

Ahmed²,

Wang³

et al. 2021

Proceedings 2021 Network and Distributed System Security Symposium

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End-user-devices in the current cellular ecosystem are prone to many different vulnerabilities across different generations and protocol layers. Fixing these vulnerabilities retrospectively can be expensive, challenging, or just infeasible. A pragmatic approach for dealing with such a diverse set of vulnerabilities would be to identify attack attempts at runtime on the device side, and thwart them with mitigating and corrective actions. Towards this goal, in the paper we propose a general and extendable approach called PHOENIX for identifying nday cellular network control-plane vulnerabilities as well as dangerous practices of network operators from the device vantage point. PHOENIX monitors the device-side cellular network traffic for performing signature-based unexpected behavior detection through lightweight runtime verification techniques. Signatures in PHOENIX can be manually-crafted by a cellular network security expert or can be automatically synthesized using an optional component of PHOENIX, which reduces the signature synthesis problem to the language learning from the informant problem. Based on the corrective actions that are available to PHOENIX when an undesired behavior is detected, different instantiations of PHOENIX are possible: a full-fledged defense when deployed inside a baseband processor; a user warning system when deployed as a mobile application; a probe for identifying attacks in the wild. One such instantiation of PHOENIX was able to identify all 15 representative n-day vulnerabilities and unsafe practices of 4G LTE networks considered in our evaluation with a high packet processing speed (∼68000 packets/second) while inducing only a moderate amount of energy overhead (∼4mW).

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Learning Linear Temporal Properties

Cited by 87 publications

References 33 publications

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Survey on Learning-Based Formal Methods: Taxonomy, Applications and Possible Future Directions

Graph Temporal Logic Inference for Classification and Identification

PHOENIX: Device-Centric Cellular Network Protocol Monitoring using Runtime Verification

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