Developing Deep Learning-Enhanced Cybersecurity Protocols for Protecting Intelligent Infrastructure from Emerging Threats

Abstract

The increasing adoption of intelligent infrastructure, integrating IoT devices, smart sensors, and interconnected systems, has significantly enhanced the efficiency and functionality of urban environments. However, this interconnectivity also introduces complex cybersecurity challenges, exposing these infrastructures to sophisticated and evolving threats. Traditional cybersecurity measures often fall short in addressing these dynamic risks. Deep learning offers advanced capabilities for enhancing cybersecurity protocols through real-time threat detection, anomaly detection, and automated response strategies. This paper explores the application of deep learning in developing enhanced cybersecurity protocols to protect intelligent infrastructure from emerging threats. We discuss various deep learning techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), and their roles in identifying malicious activities, detecting vulnerabilities, and responding to cyberattacks. We also address challenges related to data quality, model interpretability, and integration with existing cybersecurity frameworks. By leveraging deep learning, intelligent infrastructure can achieve improved security, resilience, and adaptability, safeguarding critical systems against current and future threats.