Parallel Physics-Informed Neural Networks & Giza Pyramid Construction Optimization Algorithm for FDIAs Detection in Industrial IoT:ML Based Techniques
Basi Reddy A, R. Yogesh Rajkumar
Abstract
The rapid evolution of IIoT (Industrial Internet of Things) in computing has brought about numerous security concerns, among which is the looming threat of False Data Injection (FDI) attacks. To address these attacks, a study introduces a novel approach called MLBT-FDIA-IIoT (Fault Data Injection Attack Detection in IIoT using Parallel Physics-Informed Neural Networks with Giza Pyramid Construction Optimization algorithm). This method makes use of real-time sensor data for attack detection. The data is preprocessed using Distributed Set-Membership Fusion Filtering (DSMFF) to remove noise. Then, it is fed into a neural network for classification. Specifically, Parallel Physics-Informed Neural Networks (PPINN) are used to distinguish between normal operations and False Data Injection Attacks (FDIAs). However, PPINN lacks optimization methods for accurate detection. To address this, the study proposes the Giza Pyramid Construction Optimization algorithm (GPCOA). This algorithm optimizes the PPINN classifier to detect attacks with more precision. The proposed MLBT-FDIA-IIoT method is implemented using MATLAB and evaluates various metrics such as accuracy, recall, and precision. The results demonstrate significant improvements compared to existing techniques such as MLT-FDI-IIoT, FDIA-FDAS-IIoT, and DCDD-IIoT-FDIA.