Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems

Autor: Yadigar Imamverdiyev, Rasim M. Alguliyev, Lyudmila V. Sukhostat
Rok vydání: 2021
Předmět:
Zdroj: Neural Computing and Applications. 33:10211-10226
ISSN: 1433-3058
0941-0643
DOI: 10.1007/s00521-021-05785-2
Popis: 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.
Databáze: OpenAIRE
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