A network traffic prediction model of smart substation based on IGSA‐WNN

Xia, Xin; Liu, Xiaofeng; Lou, Jichao

doi:10.4218/etrij.2019-0040

Cited by 10 publications

(4 citation statements)

References 35 publications

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“…The conventional GSA, while effective in searching for algorithmic parameters in the RBFNN, exhibits relatively slow updates of gravitational coefficients and iteration speeds. In order to expedite this process, we propose an IGSA as a means to overcome these limitations [60]. The objective of IGSA is to enhance the gravitational coefficient, update speed formula, and position update formula of the regular GSA.…”

Section: Lagrange's Ahm-critic Coefficient Couplingmentioning

confidence: 99%

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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Accurate measurement of systemic financial risk is crucial for maintaining the stability of financial markets. Taking China as the subject of investigation, the Chinese Financial Stress Index (CFSI) indicator system was constructed by integrating six dimensions and employing Gray Relation Analysis (GRA) to reduce the dimensionality of the indicators. The CFSI was derived using the Attribute Hierarchy Model (AHM) method with the Criteria Importance Through the Intercriteria Correlation (CRITIC) method, and an Improved Gravitational Search Algorithm (IGSA)-optimized Radial Basis Function Neural Network (RBFNN) was proposed for out-of-sample prediction of CFSI trends from 2024 to 2026. By analyzing the trends in financial pressure indicators, the intricate relationship between financial pressure and economic activity can be effectively discerned. The research findings indicate that (1) the CFSI is capable of accurately reflecting the current financial stress situation in China, and (2) the IGSA-RBFNN demonstrates strong robustness and generalization capabilities, predicting that the CFSI index will reach a peak value of 0.543 by the end of 2024, and there exists a regular pattern of stress rebound towards the end of each year. The novel methodology enables policymakers and regulatory authorities to proactively identify potential risks and vulnerabilities, facilitating the formulation of preventive measures.

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Section: Lagrange's Ahm-critic Coefficient Couplingmentioning

confidence: 99%

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Tian,

2024

Mathematics

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“…At present, the main processing method for industrial control system network misuse detection is in order to use deep learning network to learn the characteristics of industrial control system network data, and complete the classification by Softmax classifier. Early researchers mainly used Wavelet Neural Network (WNN) and Deep Neural Network (DNN) to establish misuse detection models [5,6]. However, they find that there are some problems in establishing network misuse detection models of industrial control systems by using such networks.…”

Section: Explainationsmentioning

confidence: 99%

Network Intrusion Detection Based on Explainable Artificial Intelligence

Wang

Liu

et al. 2023

Wireless Pers Commun

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People often use similar methods to invade network traffic, such as flood attacks and Ddos attacks. Early detection of malicious traffic usually uses manual filtering to establish a whitelist, and then uses some artificial intelligence to simply measure some characteristic parameters. The high predictive performance of artificial intelligence will inevitably introduce unknowable decision process into network intrusion detection. When it is applied to the extremely important network environment, human's doubt on its black box effect will hinder its advancement. Here, we make a propose to come up with a network traffic intrusion detection method (XAI-IDS) on account of interpretable artificial intelligence to detect malicious traffic intrusion in networks. XAI-IDS analyzes network traffic data to predict whether the traffic is malicious intrusion.

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“…11 With the development of network measurement technology, researchers have found that network traffic has a long-term correlation, namely, self-similarity, which cannot be dealt with by these traditional linear prediction model models. The nonlinear prediction model include support vector machine (SVM), 12 least square support vector machine (LSSVM), 13,14 artificial neural networks, [15][16][17][18] and gray model. 19 In recent years, some network traffic models based on deep learning model are proposed and achieve some progress.…”

Section: Introductionmentioning

confidence: 99%

Network traffic prediction method based on autoregressive integrated moving average and adaptive Volterra filter

Tian

2021

Int J Communication

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In the process of controlling network congestion and network management, how to accurately predict network traffic is very important. A network traffic prediction method based on autoregressive integrated moving average (ARIMA) model and adaptive Volterra filter model is proposed in this study. Firstly, ARIMA model is utilized to capture and predict the linear features of network traffic time series. The residual sequence is generated by network traffic sequence minus ARIMA predictive value sequence. The residual sequence only contains the nonlinear characteristics of original network traffic. Then an adaptive Volterra filter is used to train and predict the nonlinear residual sequence of original network traffic. The optimal parameters of adaptive Volterra filter are obtained by improved particle swarm optimization algorithm. Add the linear value predicted by ARIMA and the nonlinear value predicted by adaptive Volterra filter to get the final predicted value. The simulation results show that the proposed prediction method has high accuracy, small prediction error, and high reliability.

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A network traffic prediction model of smart substation based on IGSA‐WNN

Cited by 10 publications

References 35 publications

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Systemic Financial Risk Forecasting: A Novel Approach with IGSA-RBFNN

Network Intrusion Detection Based on Explainable Artificial Intelligence

Network traffic prediction method based on autoregressive integrated moving average and adaptive Volterra filter

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