Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Wu, Min; Song, Zhengyi; Moon, Young B.

doi:10.1007/s10845-017-1315-5

Cited by 166 publications

(68 citation statements)

References 19 publications

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“…An ML approach to physical data was proposed in Reference [87] for cyber-physical attack detection. Two examples of attacks were considered and simulations, along with experimental demonstrations were carried out to verify the effectiveness of the proposed method on cybermanufacturing system (CMS) security.…”

Section: Security and Threat Detectionmentioning

confidence: 99%

“…Moustafa et al, 2018 [81] Monitoring and detection of cyber-attacks in Industry 4.0 MHMM Wu et al, 2019 [87] Detection of cyber-physical attacks in 3-D printing processes KNN, RandF, and anomaly detection Park et al, 2018 [88] Detection of anomalies in multi-variety production systems DNN Keliris et al, 2016 [89] Detection of abnormalities and malicious activities SVM…”

Section: Target Of Security Mechanisms ML Solutionmentioning

confidence: 99%

See 1 more Smart Citation

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

216

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The recent advancements in the fields of artificial intelligence (AI) and machine learning (ML) have affected several research fields, leading to improvements that could not have been possible with conventional optimization techniques. Among the sectors where AI/ML enables a plethora of opportunities, industrial manufacturing can expect significant gains from the increased process automation. At the same time, the introduction of the Industrial Internet of Things (IIoT), providing improved wireless connectivity for real-time manufacturing data collection and processing, has resulted in the culmination of the fourth industrial revolution, also known as Industry 4.0. In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions. We start by examining various proposed cloud/fog/edge architectures, highlighting their importance for acquiring manufacturing data in order to train the ML algorithms. In addition, as faults might also occur from sources beyond machine degradation, the potential of ML in safeguarding cyber-security is thoroughly discussed. Moreover, a major concern in the Industry 4.0 ecosystem is the role of human operators and workers. Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors. Finally, open issues in these relevant fields are given, stimulating further research.

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Section: Security and Threat Detectionmentioning

confidence: 99%

Section: Target Of Security Mechanisms ML Solutionmentioning

confidence: 99%

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

216

View full text Add to dashboard Cite

show abstract

“…85,88 Review algorithm, which is a typical decision tree ML structure, was applied to a CNC milling process to successfully detect cyber-physical attacks with 91.1% accuracy. 89 Another study developed a bridge crack detection system with an active contour model and SVM to recognize and evaluate material failures. 90 State-of-the-art algorithms explored in this review have shown their capability in solving critical problems in different types of manufacturing methods, and it is believed that algorithmically driven methods hold huge potential and great promise in the development of Industry 4.0 as the next generation of the industrial revolution.…”

Section: Challenges and Perspectivementioning

confidence: 99%

Machine Learning for Advanced Additive Manufacturing

Jin

Zhang

Demir

et al. 2020

Matter

150

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“…The study successfully described the future potential on how sensor-based monitoring techniques can be used for realtime quality monitoring in the manufacturing industry (Roberson III, 2016). Wu et al (2017a) further their study on Wu et al (2016) to investigate how to detect cyber-physical attacks in cyber-manufacturing system using ML techniques. Two case studies were used: (1) Computer numerical control (CNC) milling machine and (2) additive manufacturing (AM) machine.…”

Section: Recent Applications Of Machine Learning With Big Data In Addmentioning

confidence: 99%

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

Alabi¹,

Botef²

2018

American Journal of Engineering and Applied Sciences

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Additive Manufacturing (AM) which is also known as 3D printing technology; is recognized as a new paradigm for manufacturing industry. Additive manufacturing is rapidly expanding across different sectors such as healthcare, electronics, automotive, science and engineering, education, dental, etc. Machine Learning and Big Data are both emerging technologies which are becoming popular and gaining more attention from the industries and academic. Machine Learning is a growing field of Artificial Intelligence (AI) that allows systems to learn from data, identify patterns and make decisions with very little human involvement. On the other hand, Big Data is referred to as datasets whose size is more than the capacity of what a conventional database software tools can capture, store, manage and analyze. Lately, Machine Learning techniques and Big Data Analytics are being applied to various applications of additive manufacturing to monitor building process and enhance decisions making using data generated through different sensors or cameras. This paper explores recent applications of Machine Learning with Big Data in the field of additive manufacturing, for instance, application of machine learning in detecting defect or anomaly during build process in additive manufacturing/3D printing machine.

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Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods

Cited by 166 publications

References 19 publications

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Machine Learning for Advanced Additive Manufacturing

A Survey on Recent Applications of Machine Learning with Big Data in Additive Manufacturing Industry

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