Monitoring is a highly used technique to check cyber-physical systems(CPS), where the corresponding model is either unavailable or not worthwhile to obtain.q Based on observing executions of a system, a monitor analyses if observations comply with expected behavior. Predictive monitoring of CPSs is an attempt to add prediction capability to this verification platform, where the history of a system is learned by training over previous traces. We seek to enhance the flexibility of the statistical monitor to make it feasible to be used in the CSPs. These models can be used in safety-critical systems to inform the imminent fault in advance. In this thesis, we proposed a statistical predictive run-time verification that is equipped with dynamic horizon allocation. Therefore, based on the received observations, the monitor can increase the prediction window when it gets a trace similar to the one already seen in the historical data log. On the other hand, the monitor switches to a more conservative short window prediction zone when receiving an unseen data stream. This feature adds flexibility to the solution allowing a solution to be used in a different environment with different degrees of predictability. Another contribution is to use background knowledge(BK) to enhance the prediction capability of a system while running the monitor online. It was demonstrated that BK contributes positively to increasing the prediction zone while decreasing the error. We also suggested that the hidden Markovian model(HMM), used as the system's history, can be updated while receiving the new trace. We considered both adaptations of the parameters and modification of the structure on the fly.

Predictive Statistical Monitoring(Improvements on online learning and background knowledge) / Beirami, Hani. - (2023 Jan 31), pp. 1-128. [10.15168/11572_369627]

Predictive Statistical Monitoring(Improvements on online learning and background knowledge)

Beirami, Hani
2023-01-31

Abstract

Monitoring is a highly used technique to check cyber-physical systems(CPS), where the corresponding model is either unavailable or not worthwhile to obtain.q Based on observing executions of a system, a monitor analyses if observations comply with expected behavior. Predictive monitoring of CPSs is an attempt to add prediction capability to this verification platform, where the history of a system is learned by training over previous traces. We seek to enhance the flexibility of the statistical monitor to make it feasible to be used in the CSPs. These models can be used in safety-critical systems to inform the imminent fault in advance. In this thesis, we proposed a statistical predictive run-time verification that is equipped with dynamic horizon allocation. Therefore, based on the received observations, the monitor can increase the prediction window when it gets a trace similar to the one already seen in the historical data log. On the other hand, the monitor switches to a more conservative short window prediction zone when receiving an unseen data stream. This feature adds flexibility to the solution allowing a solution to be used in a different environment with different degrees of predictability. Another contribution is to use background knowledge(BK) to enhance the prediction capability of a system while running the monitor online. It was demonstrated that BK contributes positively to increasing the prediction zone while decreasing the error. We also suggested that the hidden Markovian model(HMM), used as the system's history, can be updated while receiving the new trace. We considered both adaptations of the parameters and modification of the structure on the fly.
31-gen-2023
XXIV
2021-2022
Ingegneria e scienza dell'Informaz (29/10/12-)
Information and Communication Technology
no
Inglese
File in questo prodotto:
File Dimensione Formato  
PhD-Thesis.pdf

embargo fino al 31/01/2025

Tipologia: Tesi di dottorato (Doctoral Thesis)
Licenza: Tutti i diritti riservati (All rights reserved)
Dimensione 2.6 MB
Formato Adobe PDF
2.6 MB Adobe PDF   Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/369627
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact