Fuzzy Information Granulation and ED-LSTM based Traffic Prediction of Industrial Control Systems

The industrial control system (ICS) is facing increasing threats in underline communication infrastructure. The mathematical model of communication network traffic in ICS plays a crucial part in the precaution of the cyberattacks. To this end, this paper proposes an integrated prediction approach using learning rate exponential decay (ED-LSTM) method and fuzzy information granulation. The proposed prediction approach is designed to characterize the traffic patterns of ICS for both point and interval prediction. The traffic pattern prediction is essential to characterize the operation behaviors and unique traits in ICS. The experiments and numerical results demonstrate that the proposed integrated prediction approach outperforms the other prediction models in both point prediction and interval prediction.

[1]  J. Sola,et al.  Importance of input data normalization for the application of neural networks to complex industrial problems , 1997 .

[2]  Witold Pedrycz,et al.  The design of fuzzy information granules: Tradeoffs between specificity and experimental evidence , 2009, Appl. Soft Comput..

[3]  Dawei Chen,et al.  Research on Traffic Flow Prediction in the Big Data Environment Based on the Improved RBF Neural Network , 2017, IEEE Transactions on Industrial Informatics.

[4]  Maciej Szmit,et al.  Usage of Pseudo-estimator LAD and SARIMA Models for Network Traffic Prediction: Case Studies , 2012, CN.

[5]  Jinde Cao,et al.  Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals , 2017 .

[6]  Weijia Lu,et al.  Parameters of Network Traffic Prediction Model Jointly Optimized by Genetic Algorithm , 2014, J. Networks.

[7]  Wei Ruan,et al.  FARIMA model-based communication traffic anomaly detection in intelligent electric power substations , 2019, IET Cyper-Phys. Syst.: Theory & Appl..

[8]  Wenjuan Wang,et al.  Research on Traffic Recognition Algorithms for Industrial Control Networks based on Deep Learning , 2019, Proceedings of the 3rd International Conference on Mechatronics Engineering and Information Technology (ICMEIT 2019).

[9]  Stephen Graham Ritchie,et al.  TRANSPORTATION RESEARCH. PART C, EMERGING TECHNOLOGIES , 1993 .

[10]  Jin Wang.  A process level network traffic prediction algorithm based on ARIMA model in smart substation , 2013, 2013 IEEE International Conference on Signal Processing, Communication and Computing (ICSPCC 2013).

[11]  Shui Yu,et al.  Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks , 2018, Wirel. Commun. Mob. Comput..

[12]  Biqing Zeng,et al.  Network Traffic Prediction Model Based on Auto-regressive Moving Average , 2014, J. Networks.

[13]  Jiao Jiao,et al.  Analysis of Industrial Control Systems Traffic Based on Time Series , 2015, 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems.

[14]  Min Wei,et al.  Intrusion detection scheme using traffic prediction for wireless industrial networks , 2012, Journal of Communications and Networks.

[15]  Héctor Pomares,et al.  Hybridization of intelligent techniques and ARIMA models for time series prediction , 2008, Fuzzy Sets Syst..

[16]  Janusz Kolbusz,et al.  Network Traffic Model for Industrial Environment , 2005, IEEE Transactions on Industrial Informatics.

[17]  Adriano Valenzano,et al.  Review of Security Issues in Industrial Networks , 2013, IEEE Transactions on Industrial Informatics.

[18]  Yunpeng Wang,et al.  Long short-term memory neural network for traffic speed prediction using remote microwave sensor data , 2015 .