Advancing Threat Detection in Fog Computing: A Comprehensive Approach to Real-Time Analysis and Model Generation

Fog computing, an extension of cloud computing, places computational resources closer to data sources, enhancing network efficiency and security. This paper addresses the challenges posed by cybersecurity threats within fog computing environments, specifically in managing vast volumes of network traffic from IoT devices which are vulnerable to cyberattacks. We propose a novel architecture for real-time anomaly detection that integrates fog and cloud computing layers. This two-tiered system utilizes machine learning and deep learning models for dynamic and adaptive threat detection. By leveraging the computational power of modern GPUs, the system analyzes network traffic at speeds up to 10 Gbps, enabling the detection of anomalies before they can cause significant damage. We introduce a distributed anomaly supervisor that operates at the fog level to provide real-time analysis and immediate response to potential threats, while cloud-level operations facilitate a comprehensive overview and coordination. The effectiveness of this architecture is demonstrated through extensive experimentation, proving its capability to manage high-speed data transfers and improve security measures across fog networks.