A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems

Moustafa, Nour; Adi, Erwin; Turnbull, Benjamin; Hu, Jiankun

doi:10.1109/access.2018.2844794

Cited by 125 publications

(75 citation statements)

References 37 publications

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“…More specifically, threat intelligence enables efficient event and network traffic monitoring and analysis, using a signature-based method exploiting a predefined blacklist of existing attack signatures, an anomaly-based method taking advantage of profiles of normal events to recognize attacks or a hybrid technique, where an anomaly generally defines something that visibly deviates from the norm/standard and can be identified by analysing the sensed data or traffic patterns. In Reference [81], a novel threat intelligence scheme based on beta mixture-hidden Markov models (MHMMs) was proposed that intends to model the dynamic interactions of Industry 4.0 subsystems and discover known and unknown attacks, while surpassing existing signature-and anomaly-based methods. In this scheme, Industry 4.0 key elements, that is, CPS and IoT, interact and two principal components are included; a smart management module, handling the heterogeneous data sources of sensors, actuators and network nodes and a threat intelligence module monitoring and indicating abnormal activities and cyber-attacks in the physical and network domains.…”

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

238

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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

238

View full text Add to dashboard Cite

show abstract

“…", are still at the initial stage. However, many of these publications are devoted to the problems of processing big data that are collected during the operation of such systems [19][20][21][22][23][24][25].…”

Section: Resultsmentioning

confidence: 99%

Cyber-Physical Systems In Construction For Sustainable Urban Development

Volkov

Shilova

2020

E3S Web Conf.

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The article reviews the current state of research works in the field of cyber-physical systems development in conjunction with «smart house» technology. The authors have collected statistical data on the articles and the papers about cyber-physical systems in construction from 2009 to 2019, which are presented in data base SCOPUS. These publications were analysed by the authors and as the result the countries leader in this field of studying were identified. The authors formulate a definition of a building cyber-physical system and determine the operating conditions of such systems. Finally, further research objectives were identified by the researchers.

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“…The detection rates for the attack types Analysis, Backdoor, DoS, Exploits, Fuzzer, Generic, Normal, Reconnaissance, Shellcode and Worms are 83.3%, 91.8%, 95.1%, 96%, 60%, 99.5%, 98.9%, 96.8%, 81.1% and 76% respectively. Moustafa et al [38] proposed a new threat intelligence scheme. It models the dynamic interactions of industry 4.0 component including physical and network systems.…”

Section: International Journal Of Engineering and Advanced Technologymentioning

confidence: 99%

Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems

Sonule¹,

Kalla²,

Jain³

et al. 2020

IJEAT

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The network attacks become the most important security problems in the today’s world. There is a high increase in use of computers, mobiles, sensors,IoTs in networks, Big Data, Web Application/Server,Clouds and other computing resources. With the high increase in network traffic, hackers and malicious users are planning new ways of network intrusions. Many techniques have been developed to detect these intrusions which are based on data mining and machine learning methods. Machine learning algorithms intend to detect anomalies using supervised and unsupervised approaches.Both the detection techniques have been implemented using IDS datasets like DARPA98, KDDCUP99, NSL-KDD, ISCX, ISOT.UNSW-NB15 is the latest dataset. This data set contains nine different modern attack types and wide varieties of real normal activities. In this paper, a detailed survey of various machine learning based techniques applied on UNSW-NB15 data set have been carried out and suggested thatUNSW-NB15 is more complex than other datasets and is assumed as a new benchmark data set for evaluating NIDSs.

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A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems

Cited by 125 publications

References 37 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

Cyber-Physical Systems In Construction For Sustainable Urban Development

Unsw-Nb15 Dataset and Machine Learning Based Intrusion Detection Systems

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