A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning

Park, Seong-Taek; Li, Guozhong; Hong, Jingping

doi:10.1007/s12652-018-0998-6

Cited by 75 publications

(41 citation statements)

References 14 publications

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“…In Reference [88], a cost-effective context-aware DNN-based intrusion detection method was presented. This solution consisted of several hidden layers between the input and output layers and it was applied in smart factories, threatened by a plethora of advanced cyber-security threats.…”

Section: Security and Threat Detectionmentioning

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

242

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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%

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

Angelopoulos

Michailidis

Νομικός

et al. 2019

Sensors

242

View full text Add to dashboard Cite

show abstract

“…Recently, context-aware and ensemble learning methods are widely considered due to their high performance. For instance, the authors in [ 13 ] proposed a context-aware intrusion detection system utilizing machine learning for a smart factory, and the study in [ 14 ] used an ensemble machine learning approach for disease diagnosis based on wearable sensors. The authors in [ 15 ] suggested an ensemble learning-based algorithm named Random Forest (RF) for real-time fault detection in magnetic position sensors.…”

Section: Related Workmentioning

confidence: 99%

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Saeed

Lee

Jan

et al. 2021

Sensors

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Sensors’ existence as a key component of Cyber-Physical Systems makes it susceptible to failures due to complex environments, low-quality production, and aging. When defective, sensors either stop communicating or convey incorrect information. These unsteady situations threaten the safety, economy, and reliability of a system. The objective of this study is to construct a lightweight machine learning-based fault detection and diagnostic system within the limited energy resources, memory, and computation of a Wireless Sensor Network (WSN). In this paper, a Context-Aware Fault Diagnostic (CAFD) scheme is proposed based on an ensemble learning algorithm called Extra-Trees. To evaluate the performance of the proposed scheme, a realistic WSN scenario composed of humidity and temperature sensor observations is replicated with extreme low-intensity faults. Six commonly occurring types of sensor fault are considered: drift, hard-over/bias, spike, erratic/precision degradation, stuck, and data-loss. The proposed CAFD scheme reveals the ability to accurately detect and diagnose low-intensity sensor faults in a timely manner. Moreover, the efficiency of the Extra-Trees algorithm in terms of diagnostic accuracy, F1-score, ROC-AUC, and training time is demonstrated by comparison with cutting-edge machine learning algorithms: a Support Vector Machine and a Neural Network.

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“…Many studies (Pereira et al 2017;Wang et al 2018;Zhao and Fan 2018) have explored how the quality of OGD can help developers reduce the time and cost of information system development. Machine learning technology is widely used in various information systems to enhance intelligence (Demarie and Sabia 2019;Kumar et al 2019;Lee and Park 2019;Liu et al 2018;Park et al 2020;Sharp et al 2018;Xiao et al 2018). Wireless sensor network (WSN) is the foundation of the Internet of Things (IoT).…”

Section: Related Workmentioning

confidence: 99%

Integrating machine learning and open data into social Chatbot for filtering information rumor

Hsu

Chang

2020

J Ambient Intell Human Comput

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Social networks have become a major platform for people to disseminate information, which can include negative rumors. In recent years, rumors on social networks has caused grave problems and considerable damages. We attempted to create a method to verify information from numerous social media messages. We propose a general architecture that integrates machine learning and open data with a Chatbot and is based cloud computing (MLODCCC), which can assist users in evaluating information authenticity on social platforms. The proposed MLODCCC architecture consists of six integrated modules: cloud computing, machine learning, data preparation, open data, chatbot, and intelligent social application modules. Food safety has garnered worldwide attention. Consequently, we used the proposed MLODCCC architecture to develop a Food Safety Information Platform (FSIP) that provides a friendly hyperlink and chatbot interface on Facebook to identify credible food safety information. The performance and accuracy of three binary classification algorithms, namely the decision tree, logistic regression, and support vector machine algorithms, operating in different cloud computing environments were compared. The binary classification accuracy was 0.769, which indicates that the proposed approach accurately classifies using the developed FSIP.

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A study on smart factory-based ambient intelligence context-aware intrusion detection system using machine learning

Cited by 75 publications

References 14 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

CAFD: Context-Aware Fault Diagnostic Scheme towards Sensor Faults Utilizing Machine Learning

Integrating machine learning and open data into social Chatbot for filtering information rumor

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