In the context of the Industrial Internet of Things (IIoT), cybersecurity refers to preventing unauthorized access, attacks, and vulnerabilities to interconnected devices, networks, and data. Given the inherent interconnectedness of IIoT devices, ensuring security is of paramount importance to mitigate potential disruptions, data breaches, and malicious activities. As IIoT systems continue to proliferate, the significance of robust security measures, effective intrusion detection, and intelligent detection techniques escalates to safeguard critical infrastructure and sensitive data from cyber threats. This work aims to contribute towards establishing a secure and resilient industrial environment through the utilization of a hybrid model: Convolutional Neural Network with Deep Neural Network, accommodating distinct class distributions. The recent “Edge IIoTset” dataset is harnessed to enhance the model efficacy. Throughout the evaluation process, diverse metrics are employed, encompassing Accuracy, Precision, Recall, and the F1-score. By applying thorough preprocessing and using various class distribution scenarios (2, 6, 9, 10, and 15 classes), the model achieved excellent classification results. Notably, the 9-class configuration reached an Accuracy of 99.13 %, while the 6-class and 10-class setups also delivered strong performance at 97.13 % and 96.11 %, respectively. Our architecture effectively combines feature extraction and deep classification layers, resulting in a robust solution adaptable to complex IIoT traffic.