Alireza Souri
Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications
Souri, Alireza; Mohammed, Amin Salih; Yousif Potrus, Moayad; Malik, Mazhar Hussain; Safara, Fatemeh; Hosseinzadeh, Mehdi
Authors
Amin Salih Mohammed
Moayad Yousif Potrus
Dr Mazhar Malik Mazhar.Malik@uwe.ac.uk
Associate Director Intelligent Systems
Fatemeh Safara
Mehdi Hosseinzadeh
Abstract
By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies.
Citation
Souri, A., Mohammed, A. S., Yousif Potrus, M., Malik, M. H., Safara, F., & Hosseinzadeh, M. (2020). Formal verification of a hybrid machine learning-based fault prediction model in Internet of Things applications. IEEE Access, 8, 23863-23874. https://doi.org/10.1109/ACCESS.2020.2967629
Journal Article Type | Article |
---|---|
Acceptance Date | Jan 13, 2020 |
Online Publication Date | Jan 20, 2020 |
Publication Date | Feb 6, 2020 |
Deposit Date | Nov 10, 2022 |
Publicly Available Date | Mar 29, 2024 |
Journal | IEEE Access |
Electronic ISSN | 2169-3536 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Peer Reviewed | Peer Reviewed |
Volume | 8 |
Pages | 23863-23874 |
DOI | https://doi.org/10.1109/ACCESS.2020.2967629 |
Keywords | Internet of Things applications, fault prediction, formal verification, process analysis toolkit, multi-layer perceptron, particle swarm optimization, Data Mining for Internet of Things |
Public URL | https://uwe-repository.worktribe.com/output/10130492 |
Publisher URL | https://ieeexplore.ieee.org/abstract/document/8963605 |
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