Securing the Edge: A Review of Security as a Service in MEC Environments

Abstract

The integration of Security as a Service (SecaaS) with Multi-Access Edge Computing (MEC) infrastructure represents a transformative advancement in securing distributed systems, particularly in IoT and edge environments. This research focuses on leveraging MEC's low-latency and decentralized architecture to develop scalable and resilient security solutions. The necessity for this research arises from the growing proliferation of IoT devices and real-time applications, which are increasingly vulnerable to sophisticated cyber threats. MEC’s proximity-based computation offers unique advantages for implementing robust security measures tailored to resource-constrained environments. This study reviews methodologies such as adaptive resource orchestration, knowledge distillation, transfer learning, and machine learning-enhanced intrusion detection systems (NIDS). Additionally, advanced techniques like Attribute-Based Encryption (ABE) and layered security architectures are examined for their effectiveness in addressing latency-sensitive and privacy-related challenges in MEC contexts. Comparative analyses demonstrate improvements in anomaly detection, scalability, and real-time responsiveness achieved through these approaches. The findings underscore MEC's potential to enhance security scalability and responsiveness while mitigating evolving threats. However, gaps remain in integrating these methodologies into a unified SecaaS-MEC framework. Future directions emphasize incorporating AI-driven analytics and advanced cryptographic techniques to optimize SecaaS for diverse edge computing applications.