ITI əməkdaslarının elmi isləri Elektron kitabxana Konfranslar İnformasiya Sistemi Qəzetlər UOT 004
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 Biblioqrafik təsvir
 Alguliyev , R.M. Hybrid DeepGCL model for cyber-attacks detection on cyber-physical systems / R.M. Alguliyev , Y.N. Imamverdiyev , L.V. Sukhostat // Neural Computing and Applications. - 2021. - N: 11. Vol.33.- P. 1-16.
 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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