GRIDSHIELD AI

Computer Engineering

Supervisory Control and Data Acquisition (SCADA) systems play a pivotal role in monitoring and controlling critical infrastructure across various sectors, including energy, water, and manufacturing. As these systems increasingly become targets of cyber threats, there is a pressing need for intelligent and robust security mechanisms. This project proposes an advanced cybersecurity framework employing the Meta Heuristic Supervised Preprocessing Network (MSPPNET) to enhance the detection and prevention of malicious activities within SCADA environments. The methodology begins with the acquisition of raw datasets followed by preprocessing and normalization to ensure data consistency and quality. Key features are extracted using intelligent selection mechanisms, and multiple models—including SVM, ANN, and the proposed MSPPNET-based CNN—are trained and evaluated. The MSPPNET framework incorporates meta heuristic optimization techniques into the supervised learning pipeline, improving the adaptability and accuracy of the model in identifying subtle attack patterns. Extensive experimentation and performance evaluation highlight the effectiveness of MSPPNET in achieving high detection accuracy while minimizing false positives. The final model is validated using test data and stored for real-time deployment. This approach not only improves threat detection capabilities in SCADA systems but also contributes to the development of more resilient critical infrastructure cybersecurity solutions

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.