Artificial Intelligence Techniques for Securing Fog Computing Environments: Trends, Challenges, and Future Directions

Abstract

Fog computing, an extension of cloud computing, enhances capabilities by processing data closer to the source, thereby addressing latency and bandwidth issues inherent in traditional cloud models. However, the integration of Artificial Intelligence (AI) into fog computing introduces challenges, particularly in resource management, security, and privacy. This paper systematically reviews AI applications within fog computing environments, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to ensure rigorous analysis. The studies were selected based on predefined inclusion criteria, including research published between 2010 and 2024 in peer-reviewed journals and conference papers, with searches conducted in databases like IEEE Xplore, ACM Digital Library, SpringerLink, and Scopus. The review identifies critical issues such as resource constraints, transparency in AI-driven security systems, and the need for adaptable AI models to address evolving security threats. In response, innovative solutions such as lightweight AI models (e.g., Pruned Neural Networks, Quantized Models, Knowledge Distillation), Explainable AI (XAI) (e.g., Model-Agnostic Methods, Feature Importance Analysis, Rule-Based Approaches), and federated learning are proposed. Additionally, a novel taxonomy is introduced, categorizing AI techniques into resource management, security enhancement, and privacy-preserving methods, offering a structured framework for researchers and practitioners. The paper concludes that effective AI integration in fog computing is essential for developing secure, efficient, and adaptable distributed systems, with significant implications for both academia and industry.

Keywords

• Fog computing
• artificial intelligence
• resource management
• explainable AI
• privacy and security