Yuhao Wang, Yuying Li, Yanbin Sun, Yu Jiang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.1, pp. 577-605, 2023, DOI:10.32604/cmes.2023.026791 - 23 April 2023

Abstract To identify industrial control equipment is often a key step in network mapping, categorizing network resources, and attack defense. For example, if vulnerable equipment or devices can be discovered in advance and the attack path can be cut off, security threats can be effectively avoided and the stable operation of the Internet can be ensured. The existing rule-matching method for equipment identification has limitations such as relying on experience and low scalability. This paper proposes an industrial control device identification method based on PCA-Adaboost, which integrates rule matching and machine learning. We first build a More >

Ramana M. Pidaparti^1,2, Evan J. Neblett²

CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 1-10, 2007, DOI:10.3970/cmc.2007.005.001

Abstract A neural network (NN) model is developed for the analysis and prediction of the mapping between degradation of chemical elements and electrochemical parameters during the corrosion process. The input parameters to the neural network model are alloy composition, electrochemical parameters, and corrosion time. The output parameters are the degradation of chemical elements in AA 2024-T3 material. The NN is trained with the data obtained from Energy Dispersive X-ray Spectrometry (EDS) on corroded specimens. A very good performance of the neural network is achieved after training and validation with the experimental data. After validating the NN More >