Detecting False Data Injection Attacks in Industrial Internet of Things Using an Optimized Bidirectional Gated Recurrent Unit-Swarm Optimization Algorithm Model
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 2
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.
Authors and Affiliations
Nadella Sree Divya,Ramesh Vatambeti
Keywords
Related Articles
- EP ID EP731886
- DOI https://doi.org/10.56578/ataiml020203
- Views 45
- Downloads 1
A Comparative Study on AI-Based Algorithms for Cost Prediction in Pharmaceutical Transport Logistics
Pharmaceutical transport logistics, especially in humanitarian and hospital contexts, is becoming increasingly essential with a growing need to monitor associated costs. In Morocco, however, studies focusing on the cost...
Integrating Long Short-Term Memory and Multilayer Perception for an Intelligent Public Affairs Distribution Model
In the realm of urban public affairs management, the necessity for accurate and intelligent distribution of resources has become increasingly imperative for effective social governance. This study, drawing on crime data...
Enhanced Pest and Disease Detection in Agriculture Using Deep Learning-Enabled Drones
In this study, an integrated pest and disease recognition system for agricultural drones has been developed, leveraging deep learning technologies to significantly improve the accuracy and efficiency of pest and disease...
Diagnosis of Chronic Kidney Disease Based on CNN and LSTM
Kidney plays an extremely important role in human health, and one of its important tasks is to purify the blood from toxic substances. Chronic Kidney Disease (CKD) means that kidney begins to lose its function gradually...
Enhanced Color Image Encryption Utilizing a Novel Vigenere Method with Pseudorandom Affine Functions
In the realm of digital image security, this study presents an innovative encryption methodology for color images, significantly advancing the traditional Vigenere cipher through the integration of two extensive pseudora...