CNN2D-Based Deep Learning Framework for IoT Cyberattack Detection with CICAPT-IIOT

SCADA (supervisory control and data acquisition) systems are particularly vulnerable to the new cybersecurity threats brought forth by the IIoT's fast growth, which has revolutionized industrial automation. When it comes to advanced cyber threats like denial-of-service (DoS) as well as distributed denial-of-service (DDoS), traditional malware detection systems (IDSs) often struggle to identify and mitigate them correctly. In order to improve cybersecurity in IIoT settings, this research suggests a state-of-the-art intrusion detection system (IDS) architecture that uses a CNN2D. The accuracy, recall, precision, F1-score, matrix of confusion, & AUC-ROC metrics was used to evaluate the performance of the proposed model that was trained on the CICAPT-IIoT dataset and tested on several intrusion categories. This study employs CNN2D exclusively because of its capacity to automatically extract geographical and temporal information from network traffic data, in contrast to traditional IDS methods that depend on a number of algorithms. The CNN2D model outperformed the competition when it came to recognizing malicious and benign traffic patterns, with a detection rate of 99.78% on the data used for testing. Optimal hyperparameter setups for IIoTspecific security tasks are also investigated in the research to guarantee resilience and flexibility. Based on these results, CNN2D is a great neural networks solution for current IIoT network security since it is both effective and scalable.