Deep Learning for Securing Critical Infrastructures Challenges, Innovations, and Future Directions
Naga Ramesh Palakurti · International Journal of Advanced Research in Science Communication and Technology · 2023
Deep learning has emerged as a transformative tool in enhancing the security of critical infrastructures, including energy grids, healthcare systems, transportation networks, and financial institutions. As these systems become more interconnected and digitized, they are increasingly vulnerable to cyber threats. This paper explores the role of deep learning in safeguarding these infrastructures, focusing on key models such as Convolutional Neural Networks, Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). We discuss innovative advancements such as explainable AI (XAI), federated learning, and adversarial training, which improve the transparency, scalability, and robustness of deep learning systems. The integration of deep learning with emerging technologies, including blockchain and the Internet of Things (IoT), is also explored for its potential to create decentralized and adaptive security solutions. Despite the progress made, challenges such as data privacy, scalability, and adversarial vulnerabilities remain, requiring continued research and innovation