AI-Enhanced Edge Computing: Strategies for Securing Distributed Systems

Abstract

The increasing reliance on distributed systems and edge computing in various sectors, including healthcare, smart cities, and industrial applications, has raised significant concerns regarding the security and privacy of sensitive data. This paper explores AI-enhanced edge computing strategies designed to address these security challenges. By leveraging the computational power of edge devices and AI algorithms, it is possible to enable real-time decision-making and anomaly detection while ensuring robust security measures. The paper discusses several strategies such as AI-driven intrusion detection systems, anomaly-based threat detection, encryption at the edge, and federated learning for collaborative security. The integration of AI with edge computing not only enhances system performance but also provides the ability to detect and respond to security threats with low latency. Furthermore, AI models trained locally on edge devices can ensure that sensitive data remains private, thus overcoming the limitations of centralized systems. This research highlights the role of machine learning, deep learning, and reinforcement learning in developing intelligent security solutions for edge computing environments. A case study is presented to demonstrate the practical application of these strategies in a healthcare system, where AI algorithms are used to monitor and secure the flow of medical data in real-time. The findings suggest that AI-enhanced edge computing provides a scalable, efficient, and secure framework for distributed systems, offering significant benefits in terms of data privacy, real-time threat mitigation, and system performance.