A method for remotely upgrade C8051F041 program based on CAN bus network

The CAN bus is a serial communication network using the CAN protocol. The microcontroller of C8051F041 is a highly integrated mixed-signal system-on-a-chip with an integrated CAN bus controller. The article describes in detail how to implement remote update of all MCU Flash under the same CAN bus network. The result shows that all MCU Flash in the same bus network can be remotely updated. Only CAN bus is needed for update and no additional Connected. Program maintenance and maintenance costs can be effectively reduced in industrial control networks.

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A Guide to Securing Industrial Control Networks: Integrating IT and OT Systems

IEEE Industry Applications Magazine ◽

10.1109/mias.2019.2943630 ◽

2020 ◽

Vol 26 (2) ◽

pp. 47-53

Author(s):

Richard Paes ◽

David C. Mazur ◽

Bruce K. Venne ◽

Jack Ostrzenski

Keyword(s):

Industrial Control ◽

Control Networks ◽

Industrial Control Networks

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Dynamic Defense Methods for Endogenously Secure Industrial Control Networks

2018 Chinese Automation Congress (CAC) ◽

10.1109/cac.2018.8623141 ◽

2018 ◽

Author(s):

Tianyou Yun ◽

Jun Luo ◽

Bo Peng ◽

Daojian Yao

Keyword(s):

Industrial Control ◽

Control Networks ◽

Industrial Control Networks

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Anomaly Detection for Industrial Control Networks Based on Improved One-Class Support Vector Machine

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001421500129 ◽

2020 ◽

pp. 2150012

Author(s):

Haicheng Qu ◽

Jianzhong Zhou ◽

Jitao Qin ◽

Xiaorong Tian

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Anomaly Detection ◽

Support Vector ◽

Industrial Control ◽

Control Networks ◽

Detection Algorithms ◽

Hinge Loss ◽

Feature Fitting ◽

Industrial Control Networks

In traditional network anomaly detection algorithms, the anomaly threshold needs to be defined manually. Keeping this as background, this study proposes an anomaly detection algorithm (VAEOCSVM), which combines the variable auto-encoder (VAE) and one-class support vector machine (OCSVM) to realize anomaly detection in industrial control networks. First, the VAE model is used to obtain the distribution of the original normal sample data represented by the low-dimensional code; the reconstruction error of the VAE model is merged into the new input. Then, using OCSVM’s hinge-loss objective function and the random Fourier feature fitting radial basis function (RBF) kernel method, the OCSVM model is represented and solved using the deep neural network and gradient descent method. Finally, the decision function of the OCSVM model is constructed by using the solved parameter information to realize the detection of abnormal data. The proposed algorithm is compared with other machine-learning-based anomaly detection algorithms in terms of multiple indicators such as precision, recall, and [Formula: see text] score. The experimental results using various datasets show that the proposed algorithm has a better outlier recognition ability than the machine-learning-based anomaly detection algorithms.

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