Synoptic

Beschastnikh, Ivan; Abrahamson, Jenny; Brun, Yuriy; Ernst, M.

doi:10.1145/2025113.2025188

Cited by 26 publications

(3 citation statements)

References 8 publications

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“…Among the purely algorithmic methods, the reference k-tails algorithm [17] can mine exact, but potentially complicated, Markov models of applications. This algorithm underlies the Synoptic 1 tool [9] that can learn Markov models of processes. The Invarimint tool [18] is also based on k-tails but produces simpler, socalled declarative automata, without probabilities on the edges.…”

Section: Related Workmentioning

confidence: 99%

“…Our main contributions are: (i) designing an automated technique for learning the behavior of Android applications, (ii) developing practical algorithms for building the automata, (iii) prototyping our method based on Python scripts, and (iv) evaluating its performance through extensive experiments, with a comparison to other automata learning algorithms, namely Synoptic [9] and Invarimint [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Generation of SDN policies for protecting android environments based on automata learning

Schnepf

Badonnel

Lahmadi

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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Software-defined networking offers new opportunities for protecting end users and their applications. In that context, dedicated chains can be built to combine different security functions, such as firewalls, intrusion detection systems and services for preventing data leakage. To configure these security chains, it is important to have an adequate model of the patterns that end user applications exhibit when accessing the network. We propose an automated strategy for learning the networking behavior of end applications using algorithms for generating finite state models. These models can be exploited for inferring SDN policies ensuring that applications respect the observed behavior: such policies can be formally verified and deployed on SDN infrastructures in a dynamic and flexible manner. Our solution is prototypically implemented as a collection of Python scripts that extend our Synaptic verification package. The performance of our strategy is evaluated through extensive experimentations and is compared to the Synoptic and Invarimint automata learning algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Generation of SDN policies for protecting android environments based on automata learning

Schnepf

Badonnel

Lahmadi

et al. 2018

NOMS 2018 - 2018 IEEE/IFIP Network Operations and Management Symposium

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“…Identifying both the properties and the validation techniques that are suitable for supporting the study of these properties remains open research. We have begun using a dynamic model-inference approach [2], [3], [4] to explore which simulation properties can be verified automatically during, as well as after, the simulation execution. We are interested in the degree to which this approach can be complemented by static verification of these properties.…”

Section: Simulation Validationmentioning

confidence: 99%

Resource scheduling through resource-aware simulation of emergency departments

Shin¹,

Balasubramanian²,

Brun³

et al. 2013

2013 5th International Workshop on Software Engineering in Health Care (SEHC)

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Abstract-This paper proposes using resource-aware, discreteevent simulation to measure the effects of resource scheduling in hospital emergency departments. Determining staffing and resource allocation is a complex constraint-optimization problem that has significant impact on hospital costs and patient care quality. We developed detailed models of the emergency department process of caring for patients, the resources available to support that process, and the scheduling constraints on the deployment of those resources. We then ran a battery of discrete-event simulations of this process, varying details of process, resource mixes, and scheduling constraints, to analyze the effects of resource availability (e.g., staffing patterns) on patient length of stay. Our simulation approach proved to be particularly adept at supporting the systematic investigation of two issues of particular interest to domain experts: (1) an excessive focus on minimizing the average length of stay (the objective most typically used for optimizing emergency department staffing) can have undesirable, previously unappreciated effects, (2) too strong a focus on one particular kind of resource as the preferred vehicle for decreasing patient length of stay can tend to obscure the value of considering other kinds of resources. The unexpected nature of some of our results raises open questions about how to validate the results of complex simulations.

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