TFDPM: Attack detection for cyber–physical systems with diffusion probabilistic models

Yan, Tijin; Zhou, Tong; Zhan, Yufeng; Xia, Yuanqing

doi:10.1016/j.knosys.2022.109743

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…Deep learning and machine learning techniques can extract patterns from large volumes of generated data at a speed that a human being cannot achieve. AI tools are much more suited than human operators to perform predic-Some AI technologies rely on human experts to establish a working hypothesis and to identify relevant features, but the fear of job elimination can drive human operators to be unwilling to share knowledge and provide expertise for [103,104], [115,116], [118,125], [127][128][129][130][131]133] [136-139] Future research efforts can shift focus away from process automation and toward applications that generate value with respect to analytical data insights and cognitive interaction.…”

Section: Process Automationmentioning

confidence: 99%

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Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions

Stoykova

Shakev

2023

Algorithms

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The aim of this paper is to present a systematic literature review of the existing research, published between 2006 and 2023, in the field of artificial intelligence for management information systems. Of the 3946 studies that were considered by the authors, 60 primary studies were selected for analysis. The analysis shows that most research is focused on the application of AI for intelligent process automation, with an increasing number of studies focusing on predictive analytics and natural language processing. With respect to the platforms used by AI researchers, the study finds that cloud-based solutions are preferred over on-premises ones. A new research trend of deploying AI applications at the edge of industrial networks and utilizing federated learning is also identified. The need to focus research efforts on developing guidelines and frameworks in terms of ethics, data privacy, and security for AI adoption in MIS is highlighted. Developing a unified digital business strategy and overcoming barriers to user–AI engagement are some of the identified challenges to obtaining business value from AI integration.

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Section: Process Automationmentioning

confidence: 99%

“…[ [63][64][65][66][67][68][69][70][71][72] [ [74][75][76][77][78][79][80][81], [84,87,88,90] [92,94], [95], [101][102][103][104][105][106]108] [116,117]…”

Section: Process Automationmentioning

confidence: 99%

Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions

Stoykova

Shakev

2023

Algorithms

View full text Add to dashboard Cite

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“…Rasul et al [31] introduced the hidden variables in an RNN as a priori knowledge into the DDPM denoising process to achieve the task of forecasting time series data. Yan et al [32] proposed an improved DDPM for the detection task of cyber-physical system. Although DDPM has achieved good results in areas such as image denoising, it does not require high accuracy of the generated data.…”

Section: Introductionmentioning

confidence: 99%

Missing data filling in soft sensing using denoising diffusion probability model

Jiang,

Wang,

Shen

et al. 2023

Meas. Sci. Technol.

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With the aim of addressing the problem of degradation in soft measurement accuracy due to missing data in industrial processes, a filling method based on the denoising diffusion probability model (DDPM) is proposed here to improve the accuracy of soft measurement modeling. First, missing regions are detected with the help of an improved Isolation Forest algorithm to obtain information such as the locations and numbers of missing data regions. Next, a data generation model is constructed based on DDPM and new samples are obtained. By adjusting the threshold for normal operation of the system and the weight sampler, filler samples that are similar to the distribution of the original data can be filtered from the new samples to form a complete dataset. The feasibility of the proposed missing data filling method is explored through numerical simulations, and its superiority in terms of improving the prediction accuracy of soft measurements is verified in regard to the nickel flash smelting process.

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Can Industrial Intrusion Detection Be SIMPLE?

Wolsing

Thiemt

Sloun

et al. 2022

Lecture Notes in Computer Science

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TFDPM: Attack detection for cyber–physical systems with diffusion probabilistic models

Cited by 5 publications

References 15 publications

Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions

Artificial Intelligence for Management Information Systems: Opportunities, Challenges, and Future Directions

Missing data filling in soft sensing using denoising diffusion probability model

Can Industrial Intrusion Detection Be SIMPLE?

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