Dynamic Smartcard Protection and SSELUR-GRU-Based Attack Stage Identification in Industrial IoT

Abstract

In recent years, the Industrial Internet of Things (IoT) has grown significantly. Automation along with intelligence introduces a slew of cyber risks while implementing industrial digitalization. But, none of the prevailing work focused on provoking alerts to future attacks and protecting the dynamic smart card from malicious attacks.Therefore, a Smooth Scaled Exponential Linear Unit and Reinforcement Learning-based Gated Recurrent Unit (SSELUR-GRU)-based stage identification and dynamic smart card protection are proposed in this paper.Primarily, the data pre-processing is done, and the preprocessed data are balanced using the ADASYN technique. Then, the data is clustered using the CD-KM algorithm for the feasible training of the data. After that, the clustered data is normalized and the patterns of normalized data are analyzed. Further, the important features are chosen by employing the proposed LWSO algorithm for minimizing the processing time of the classifier. Both the obtained optimal features and the patterns are data trained using Log Mish-based Pyramid Net (LM-PN), for classifying the attacked and non-attacked data. In contrast, the input data features and the attacked data are trained by using the proposed SSELUR-GRU for identifying the attack stages.Thus, based on the attack stage, the dynamic card is protected by updating its number, or else the admin is alerted.The experimental outcome stated that when analogized to prevailing methodologies, the proposed method withstands a maximum accuracy of 98.71% and a higher identification rate of 98.21%.