Cybersecurity Detection Using Neural Networks for Chemical Industry

Cybersecurity threats affect all sectors of the economy, especially the chemical industry. The chemical industry is a critical infrastructure vulnerable to cyberattacks. In addition to financial and reputational losses, they can also cause serious accidents (such as loss of control of hazardous materials) with serious consequences for workers, society, and the environment. Industrial control panel systems are typically embedded in a variety of physical devices. The safety of control systems is crucial for ensuring the secure functioning of industrial chemical processes. Nevertheless, the susceptibility of control systems to cyber-attacks has intensified due to the extensive deployment of actuators, sensor measurements, and wireless communications. These attacks have the potential to propagate swiftly and result in severe industrial accidents. This study was conducted as part of a systematic literature review. The purpose of this study is to analyze cybersecurity threats to the chemical industry. Research shows that although cyber attacks are highly vulnerable to the chemical industry, they can be detected through neural network-based integration (NN) without having to know the process model when large quantities of historical data are available and easy to apply. It has been shown that it can be done. It is hoped that the policy recommendations obtained from this research will provide more effective guidelines for protecting the chemical industry from the risks of cyber threats.