Next-Generation Iiot Security: Comprehensive Comparative Analysis of Cnn-Based Approaches

Abstract

Industrial Internet of Things (IIoT) presents a range of benefits but also introduces security vulnerabilities. This paper systematically compares different deep learning model structures for IIoT intrusion detection. Four CNN classifiers are implemented, including hybrid CNN-GRU, 1D Xception, and 1D ResNet models. The evaluation focuses on robustness and generalizability across two datasets to assess overall performance and detection rates. To handle imbalanced data, pre-processing involves PCA feature selection and undersampling. Furthermore, we design a distributed training process for massive datasets and continuous learning, enabling efficient large-scale processing. For the experimental evaluation, we make use of two datasets with multi-label classification tasks and analyze performance metrics of different models in terms of their detection abilities and efficiency. The proposed methods demonstrate strong performance, successfully identifying even rare attacks. In addition, we conduct a performance comparison with existing publications that utilize the same datasets, confirming that our models are on par with state-of-the-art IIoT models.

Keywords: Information security, Deep learning, State Assessment, Attack Detection, Industrial Internet of Things