Use of Machine Learning in Detecting Network Security of Edge Computing System

Hou, Size; Huang, Xin

doi:10.1109/icbda.2019.8713237

Cited by 20 publications

(9 citation statements)

References 10 publications

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“…In Reference [76], the advantage is efficient data handling and the disadvantages are time and space issues. Hou et al [54] proposed a security detecting network of edge computing systems using ML to solve the security problem. The advantages are providing secure data and improving security issues, and the disadvantages are network error and storage issues.…”

Section: Discussion and Lessons Learnedmentioning

confidence: 99%

“…With the expansion of general information in the cloud, there has likewise been an expansion of delicate information in the cloud, motivating the requirement for higher security in CC. This section describes methodologies proposed to improve cloud security using more accurate risk identification [54]. We begin by describing a general methodology to decide threats and dangers through the summation of hazard levels.…”

Section: And Cloud Securitymentioning

confidence: 99%

“…Hou et al [54] explained the detection of the network security of edge computing systems using ML to solve the problem. Their investigation include the manufactured simulation of a smart home framework by the Alibaba ECS.…”

Section: Svmmentioning

confidence: 99%

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A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

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Section: Discussion and Lessons Learnedmentioning

confidence: 99%

Section: And Cloud Securitymentioning

confidence: 99%

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A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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“…Once completed, (3) the results are compared between workers and the workplace to establish the appropriate design parameter, and (4) finally, when the design parameter has been selected, filters or amplifiers of variety are employed to adjust the Competences-Capacities between workers and the workplace. Each dimension that characterizes the enactive paradigm is defined by a set of variables that are identified through a questionnaire in the design phase and later in the work process through a set of sensors integrated in wearables, which are processed locally in real time (edge) to customize the environment for the worker [56,57] in the nearby environment (fog) and thus adjust to the worker the parameters of the surrogate model that allows the adaptation of the workplace to a particular worker through machine learning and other affective design algorithms [58]. Finally, data from sensors and wearables are sent to the cloud to update the surrogate model, adapting the work environment to the worker in an evolutionary way.…”

Section: Methods Techniques and Toolsmentioning

confidence: 99%

“…Figure 11 and Table 8 cover the various frameworks and algorithms in cloud, fog, and edge that have been collected from [56][57][58]82]. Firstly, this includes sensorization and data acquisition at the edge, followed by processing in fog, massive data ingestion, and its subsequent storage and treatment under cognitive computing.…”

Section: Case Studymentioning

confidence: 99%

Framework for the Development of Affective and Smart Manufacturing Systems Using Sensorised Surrogate Models

Ávila-Gutiérrez

Aguayo-González

Lama-Ruiz

2021

Sensors

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Human Factor strategy and management have been affected by the incorporation of Key Enabling Technologies (KETs) of industry 4.0, whereby operator 4.0 has been configured to address the wide variety of cooperative activities and to support skills that operate in VUCA (volatile, uncertain, complex, and ambiguous) environments under the interaction with ubiquitous interfaces on real and virtual hybrid environments of cyber-physical systems. Current human Competences-Capacities that are supported by the technological enablers could result in a radically disempowered human factor. This means that in the processes of optimization and improvement of manufacturing systems from industry 4.0 to industry 5.0, it would be necessary to establish strategies for the empowerment of the human factor, which constitute symbiotic and co-evolutionary socio-technical systems through talent, sustainability, and innovation. This paper establishes a new framework for the design and development of occupational environments 5.0 for the inclusion of singularized operators 4.0, such as individuals with special capacities and talents. A case study for workers and their inclusion in employment is proposed. This model integrates intelligent and inclusive digital solutions in the current workspaces of organizations under digital transformation.

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