Cybersecurity Fortification in Edge Computing through the Synergy of Deep Learning

This research study explores the vital realm of cybersecurity fortification in edge computing environments, focusing on the synergy of deep learning, specifically CNNs, for intrusion detection and threat mitigation. With edge computing's proliferation, the study addresses the challenges posed by resource constraints and real-time processing requirements, emphasizing the need for adaptive security measures. The research thoroughly investigates the application of CNNs for intrusion detection, encompassing system design and empirical evaluations, while also exploring their potential in automating threat mitigation responses. The recognized results demonstrate significant advancements in edge cybersecurity through CNN integration, paving the way for proactive and adaptive security measures in dynamic edge computing environments. Practical considerations are discussed, underscoring the significant advancements achieved in enhancing edge cybersecurity through CNN integration, promising proactive and adaptive security measures suitable for dynamic edge computing scenarios. Future extensions of this work involve advanced adversarial defense mechanisms, real-time threat intelligence integration, explainable deep learning models, and the exploration of federated learning for distributed intrusion detection, all contributing to a more secure and resilient future in edge computing. This research contributes a robust foundation for safer and more resilient edge deployments across diverse domains.