Detecting False Data Injection Attacks in Industrial Internet of Things Using an Optimized Bidirectional Gated Recurrent Unit-Swarm Optimization Algorithm Model

Abstract

The rapid adoption of the Industrial Internet of Things (IIoT) paradigm has left systems vulnerable due to insufficient security measures. False data injection attacks (FDIAs) present a significant security concern in IIoT, as they aim to deceive industrial platforms by manipulating sensor readings. Traditional threat detection methods have proven inadequate in addressing FDIAs, and most existing countermeasures overlook the necessity of validating data, particularly in the context of data clustering services. To address this issue, this study proposes an innovative approach for FDIA detection using an optimized bidirectional gated recurrent unit (BiGRU) model, with the Sailfish Optimization Algorithm (SOA) employed to select optimal weights. The proposed model exploits temporal and spatial correlations in sensor data to identify fabricated information and subsequently cleanse the affected data. Evaluation results demonstrate the effectiveness of the proposed method in detecting FDIAs, outperforming state-of-the-art techniques in the same task. Furthermore, the data cleaning process showcased the ability to recover damaged or corrupted data, providing an additional advantage.