Web Based Cyber Attack Detection for Industrial System (PLC) Using Deep Learning

Web apps are frequently targeted by cyberattacks due to their network access and security flaws. Instances of attacks targeting online applications might pose significant risks. The issue of cyber security continues to pose a significant barrier in Industry 5.0 scenarios, since cyber-attacks have the potential to result in severe outcomes such as production disruptions, data breaches, and even physical injuries. In order to tackle this difficulty, this study suggests a novel deep-learning approach for identifying webbased assaults in the context of Industry 5.0. The investigation explores transformer models, which are methods used in deep learning, for their capacity to accurately classify attacks and detect unusual behavior. The results of this research demonstrated the higher performance of the suggested transformer-based system, surpassing earlier methods in terms of accuracy, precision, and recall. Deep learning plays a crucial role in effectively tackling cyber security issues in Industry 5.0 settings.