期刊
NEURAL COMPUTING & APPLICATIONS
卷 33, 期 16, 页码 10211-10226出版社
SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-05785-2
关键词
Cyber-physical system; Cyber-attack detection; Deep learning; SPOCU activation function
The urgency of ensuring the security of cyber-physical systems lies in their correct functioning, which has a significant impact on various industrial sectors. This paper proposes a deep hybrid model based on three parallel neural architectures and experiments show its superiority over recent works using machine learning techniques.
The urgency of solving the problem of ensuring the security of cyber-physical systems is due to ensure their correct functioning. Cyber-physical system applications have a significant impact on different industrial sectors. The number and variety of cyber-attacks are growing, aimed not only at obtaining data from cyber-physical systems but also managing the production process itself. Detecting and preventing attacks on cyber-physical systems is critical because they can lead to financial losses, production interruptions, and therefore endanger national security. This paper proposes a deep hybrid model based on three parallel neural architectures: a one-dimensional convolutional neural network, a gated recurrent unit neural network, and a long short-term memory neural network. The SPOCU activation function is considered in hidden layers of the proposed model and improves its performance. Furthermore, to improve the classification accuracy, a modified version of Adam optimizer is considered. The experiments are conducted on two datasets: raw water treatment plant and gasoil heater loop process as the cyber-physical system applications. They contain information about the normal behavior of these systems and their failures caused by cyber-attacks. The results show that the proposed model outperforms the recent works using machine learning techniques.
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