Deep learning-based stacked models for cyber-attack detection in industrial internet of things

Abstract

Cyber-attack detection is crucial for securing Industrial Internet of Things (IIoT) systems. This study introduces advanced deep learning methodologies to identify potential cyber-attacks effectively in IIoT devices. Three novel stacked deep learning architectures, namely the StackMean, StackMax, and StackRF algorithms. These architectures aggregate and enhance the results of individual deep learning models. Specifically, StackMean computes average predicted class probabilities, StackMax selects maximum predicted class probabilities for more aggressive predictions, and StackRF leverages a random forest to aggregate base models. Theoretical analysis suggests that the proposed stacked deep learning model can boost detection accuracy compared to standalone single deep learning models. Moreover, these stacked models offer increased robustness against adversarial attacks by reducing reliance on specific neural network structures. Additionally, the synthetic minority oversampling technique (SMOTE) algorithm is integrated to address class imbalance challenges in the training dataset. Performance validation is conducted using three publicly available datasets. The detection performance is evaluated using five statistical scores. The results consistently indicate the superiority of the proposed stacked deep learning models over existing techniques. The effectiveness of the SMOTE algorithm is demonstrated through its ability to expand decision regions and minimize false negative signals during attack predictions. In addition, a statistical test is employed to compare the accuracy of individual models with the stacked models, demonstrating that the stacked models exhibit improved accuracy. By combining cutting-edge stacked deep learning architectures with strategic data augmentation techniques, this research significantly contributes to the robustness of cyber-attack detection within IIoT systems.

Topics & Concepts