Efficiently identifying deterministic real-time automata from labeled data

Verwer, Sicco; Weerdt, Mathijs de; Witteveen, Cees

doi:10.1007/s10994-011-5265-4

Cited by 22 publications

(7 citation statements)

References 25 publications

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“…Several works proposed different state-merging algorithms; Recently, Mediouni et al provided an improvement on the RTI algorithm that was proposed in [27] and with this improvement, the algorithm can learn more accurate models [13]. Pastore et al proposed the TkT algorithm that computes 𝑘 number of transitions for each state 𝑠 from the initial state machine model.…”

Section: State-merging Algorithmsmentioning

confidence: 99%

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

Cao

Blaise

Verwer

et al. 2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

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These days more companies are shifting towards using cloud environments to provide their services to their client. While it is easy to set up a cloud environment, it is equally important to monitor the system's runtime behaviour and identify anomalous behaviours that occur during its operation. In recent years, the utilisation of Recurrent Neural Networks (RNNs) and Deep Neural Networks (DNNs) to detect anomalies that might occur during runtime has been a trending approach. However, it is unclear how to explain the decisions made by these networks and how these networks should be interpreted to understand the runtime behaviour that they model. On the contrary, state machine models provide an easier manner to interpret and understand the behaviour that they model. In this work, we propose an approach that learns state machine models to model the runtime behaviour of a cloud environment that runs multiple microservice applications. To the best of our knowledge, this is the first work that tries to apply state machine models to microservice architectures. The state machine model is used to detect the different types of attacks that we launch on the cloud environment. From our experiment results, our approach can detect the attacks very well, achieving a balanced accuracy of 99.2% and a 𝐹 1 score of 0.982. CCS CONCEPTS• Computing methodologies → Anomaly detection.

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Section: State-merging Algorithmsmentioning

confidence: 99%

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

Cao

Blaise

Verwer

et al. 2022

Proceedings of the 17th International Conference on Availability, Reliability and Security

Self Cite

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“…Some other modeling formalisms for which learning algorithms have been developed include: Nondeterministic auotmata [90,91], Probabilistic automata [92,93], Petri-nets [94], Timed automata (learning timed systems 10 in active learning framework) [95,96], Buchi automata [97] and I/O automata [27].…”

Section: Miscellaneous Formalismsmentioning

confidence: 99%

Model Learning: A Survey on Foundation, Tools and Applications

Ali,

Sun,

Zhao

2019

Preprint

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The quality and correct functioning of software components embedded in electronic systems are of utmost concern especially for safety and mission-critical systems. Model-based testing and formal verification techniques can be employed to enhance the reliability of software systems. Formal models form the basis and are prerequisite for the application of these techniques. An emerging and promising model learning technique can complement testing and verification techniques by providing learned models of black box systems fully automatically. This paper surveys one such state of the art technique called model learning which recently has attracted much attention of researchers especially from the domains of testing and verification. This survey paper reviews and provides comparison summaries highlighting the merits and shortcomings of learning techniques, algorithms, and tools which form the basis of model learning. This paper also surveys the successful applications of model learning technique in multidisciplinary fields making it promising for testing and verification of realistic systems.

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“…In [134], Verwer, de Weerdt, and Witteveen present a deterministic real-time automaton (DRTA) learning algorithm, real-time identification (RTI) algorithm, utilizing event sequences that were labeled and time-stamped. The algorithm merges states based on the evidence to identify a deterministic finite-state automaton (DFA).…”

Section: Timed Automatamentioning

confidence: 99%

Verification for Machine Learning, Autonomy, and Neural Networks Survey

Xiang,

Musau,

Wild

et al. 2018

Preprint

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This survey presents an overview of verification techniques for autonomous systems, with a focus on safety-critical autonomous cyber-physical systems (CPS) and subcomponents thereof. Autonomy in CPS is enabling by recent advances in artificial intelligence (AI) and machine learning (ML) through approaches such as deep neural networks (DNNs), embedded in so-called learning enabled components (LECs) that accomplish tasks from classification to control. Recently, the formal methods and formal verification community has developed methods to characterize behaviors in these LECs with eventual goals of formally verifying specifications for LECs, and this article presents a survey of many of these recent approaches.

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Efficiently identifying deterministic real-time automata from labeled data

Cited by 22 publications

References 25 publications

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

Learning State Machines to Monitor and Detect Anomalies on a Kubernetes Cluster

Model Learning: A Survey on Foundation, Tools and Applications

Verification for Machine Learning, Autonomy, and Neural Networks Survey

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