Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System

Zhou, Xiaokang; Liang, Wei; Liu, Weimin; Yan, Ke; Shimizu, Shohei; Wang, Kevin I‐Kai

doi:10.1109/jiot.2021.3130434

Cited by 153 publications

(37 citation statements)

References 39 publications

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“…According to China's national conditions, the classification scheme of colleges and universities in UNESCO's "International Standard Classification of Education" is in line with the current situation of my country's education and talent structure, and is more likely to be recognized by people [23][24]. In the plan, colleges and universities are divided into: academic research-oriented universities which are devoted to training academic innovation and research theory-related talents; skilled colleges and universities (higher vocational colleges), which are dedicated to training talents related to crafts, practical operations, and technical industries; application-oriented universities, which are effective in training between academic research-oriented and skill-oriented, and adapt to the development needs of majors and industries.…”

Section: Fig1 Task Scheduling Process In Multi-cloud Environment 22 A...mentioning

confidence: 99%

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

Bian

Xie

2022

Preprint

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With the rapid development of Internet of things (IoT) technology and the increasing popularity of IoT devices, more and more computing intensive IoT applications came into being. However, due to the limited resources of IoT devices, cloud computing systems are required to compute intensive IoT applications. Further, in order to be subject to a single cloud computing service provider, multi-cloud computing will become an IoT service cloud computing solution. In view of the complexity of multi-cloud scheduling, the application of artificial intelligence will be an important technology to solve the multi-cloud scheduling of IoT. The corresponding talent training plays an important role in the development and implementation of the artificial intelligence multi-cloud scheduling of IoT. Firstly, this paper studies the key influencing factors of IoT’s artificial intelligence multi-cloud scheduling applied talents training. Combined with the characteristics of the development of China’s artificial intelligence industry, this paper summarizes the influencing factors from the four dimensional training path of government departments, universities, enterprises and scientific research institutes. According to the training purpose of artificial intelligence multi-cloud scheduling applied talents training, build an artificial intelligence multi-cloud scheduling applied talents training influencing factor index system. Then, the DEMATEL method is used to establish multiple correlation matrices according to the direct influence correlation between the factors, and calculate the influence degree, affected degree, center degree and cause degree of the factors; Using the improved AISM method, based on the idea of game confrontation, from the two opposite extraction rules of result priority and cause priority respectively, a group of confrontation level topological maps with comprehensive influence values reflecting the interaction of factors are obtained, and relevant suggestions are put forward in order to provide reference for the training of artificial intelligence multi-cloud scheduling applied talents.

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Section: Fig1 Task Scheduling Process In Multi-cloud Environment 22 A...mentioning

confidence: 99%

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

Bian

Xie

2022

Preprint

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“…Popular time series algorithms, including ARIMA [31], etc. [32], used wavelet decomposition and reconstruction to smooth the time series and demonstrated its feasibility for atmospheric pollutant concentration analysis. The change of environmental information involves multiple factors and has nonlinear characteristics, which significantly impacts the accuracy of environmental information prediction.…”

Section: Related Workmentioning

confidence: 99%

A Data-Secured Intelligent IoT System for Agricultural Environment Monitoring

Zhou

Xiao

Lü

et al. 2022

Wireless Communications and Mobile Computing

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Collecting environmental information of crop growth and dynamically adjusting agricultural production has been proved an effective way to improve the total agricultural yield. Agricultural IoT technology, which integrates the information sensing equipment, communication network, and information processing systems, can support such an intelligent manner in the agricultural environment. Traditional agricultural IoT could meet the service demand of small-scale agricultural production scenarios to a certain extent. However, the emerging application scenario of the agricultural environment is becoming more and more complicated, and the data nodes of the underlying access to IoT backend system are increasing in large number, while the upper-layer applications are requiring high quality of data service. Hence, the traditional architecture-based (i.e., centralised cloud computing) IoT systems suffer from the problems such as small network coverage, data security issue, and limited power supply time while attempting to provide high-quality services at the edge of the network. Emerging edge computing offers the opportunity to solve these issues. This paper builds an intelligent IoT system for agricultural environment monitoring by integrating edge computing and artificial intelligence. We conducted an experiment to validate the proposed system considering the reliability and usability. The experimental results prove the system’s reliability (e.g., data packet loss rate is less than 0.1%). The proposed system achieves the concurrency of 500TPS and the average response time of 300 ms, which meet the practical requirements in agricultural environment monitoring.

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“…There are mainly two kinds of semi-automatic ontology construction, one is based on THESAURUS [26] [27], and the other is based on top-level ontology.…”

Section: Related Workmentioning

confidence: 99%

Construction method of equipment defect domain ontology for next generation cloud computing

Yang

Zhang

et al. 2022

Preprint

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Equipment defect domain ontology is the semantic basis for constructing equipment defect knowledge graph, which can be used to organize, share, and analyze equipment defect related knowledge. At present, there are many relevant data in the field of equipment defects, such as equipment information model, equipment management documents, and equipment defect reports. These equipment defect data often focus on a single aspect of the equipment defect field. It is difficult to integrate the database with various types of equipment defect information. This paper combines the characteristics of existing equipment defect data sources to build a general equipment defect domain ontology. This research can break the barrier of multi-source heterogeneous knowledge, build an efficient storage engine for multimodal data, and empower the safety of Industrial applications, data, and platforms in multi-clouds for Internet of Things (IoT).

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Hierarchical Adversarial Attacks Against Graph-Neural-Network-Based IoT Network Intrusion Detection System

Cited by 153 publications

References 39 publications

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

Research on Influencing Factors of Artificial Intelligence Multi-cloud Scheduling Applied Talents Training based on DEMATEL-TAISM

A Data-Secured Intelligent IoT System for Agricultural Environment Monitoring

Construction method of equipment defect domain ontology for next generation cloud computing

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