Research of information security risk prediction based on grey theory and ANP

As a modern power infrastructure, smart grids have great advantages over traditional power grids, but their effective operation is largely restricted by information security. Hence, a smart grid information security risk assessment (ISRA) method is proposed. This method combines D numbers to improve the classical analytic hierarchy process (D-AHP) independent of experts’ subjective qualitative assessment and then integrated with grey theory which does not require complete and unambiguous information. First, we establish a smart grid ISRA system according to the characteristics and development reality of smart grid technology. The proposed system includes 5 first-level indexes as an intelligent terminal, a wireless communication channel, password security, application code and embedded system, and corresponding 13 secondary indexes. Second, a D-AHP method aimed at the uncertainty of human subjective judgment and fuzziness of language assessment is used to obtain the weight of each index. The D-AHP method is then combined with the grey assessment matrix solved by grey theory, to obtain the comprehensive assessment value and corresponding risk grade. With a smart grid demonstration project in Suzhou, China, as an example, an empirical study is carried out using expert scoring. The comprehensive assessment risk value is 3.8199, and the corresponding risk level is moderate. The results of this work could serve as a reference for the information security protection of smart grids.

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Prediction of Industrial Network Security Situation Based on Noise Reduction Using EMD

Zhao

Huang

et al. 2022

Mobile Information Systems

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Industrial Internet security is a prerequisite to ensure the high-quality development of the Industrial Internet. The significant way to curb Industrial Internet security accidents and prevent cyber threats proactively is effectively controlling the changes in network situations. In this paper, we propose a new prediction model based on Long Short-Term Memory (LSTM), minimum mean square variance criterion (MMSVC), and empirical mode decomposition (EMD), with the aim of effective noise reduction and high prediction accuracy. To minimize the disturbance of random noise, we firstly deleted several outliers in high-frequency and noisy Intrinsic Mode Functions (IMFs) decomposed by EMD. MMSVC performs well in identifying noisy IMFs without using thresholds. For the blank places, we refilled them by a certain weight with relevant figures. After that, the LSTM model was applied to predict the denoised signal. The preliminary experimental analysis illustrated that noise reduction with the EMD method could provide a significant boost in forecasting performance.

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Research of information security risk prediction based on grey theory and ANP

Cited by 4 publications

References 4 publications

Research on Network Security Risk Modeling and Quantitative Analysis Theory

Research on Network Security Risk Modeling and Quantitative Analysis Theory

Combination of D-AHP and Grey Theory for the Assessment of the Information Security Risks of Smart Grids

Prediction of Industrial Network Security Situation Based on Noise Reduction Using EMD

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