# The Security and Privacy of Mobile Edge Computing: An Artificial Intelligence Perspective

arXiv: https://arxiv.org/abs/2401.01589 · HTML: https://mlsanity.com/paper/2401.01589/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2024.

## Built on by (most cited first, 4 of 4)

- [Benchmarking Ultra-Low-Power μNPUs](https://mlsanity.com/paper/2503.22567/) (2025)
- [Logit Calibration and Feature Contrast for Robust Federated Learning on Non-IID Data](https://mlsanity.com/paper/2404.06776/) (2024)
- [Stable-MoE: Lyapunov-based Token Routing for Distributed Mixture-of-Experts Training over Edge Networks](https://mlsanity.com/paper/2512.06784/) (2025)
- [Transformer-Enhanced Reinforcement Learning: Fundamentals and Applications in Communication Networks](https://mlsanity.com/paper/2606.05208/) (2026)

## Abstract (arXiv)

Mobile Edge Computing (MEC) is a new computing paradigm that enables cloud computing and information technology (IT) services to be delivered at the network's edge. By shifting the load of cloud computing to individual local servers, MEC helps meet the requirements of ultralow latency, localized data processing, and extends the potential of Internet of Things (IoT) for end-users. However, the crosscutting nature of MEC and the multidisciplinary components necessary for its deployment have presented additional security and privacy concerns. Fortunately, Artificial Intelligence (AI) algorithms can cope with excessively unpredictable and complex data, which offers a distinct advantage in dealing with sophisticated and developing adversaries in the security industry. Hence, in this paper we comprehensively provide a survey of security and privacy in MEC from the perspective of AI. On the one hand, we use European Telecommunications Standards Institute (ETSI) MEC reference architecture as our based framework while merging the Software Defined Network (SDN) and Network Function Virtualization (NFV) to better illustrate a serviceable platform of MEC. On the other hand, we focus on new security and privacy issues, as well as potential solutions from the viewpoints of AI. Finally, we comprehensively discuss the opportunities and challenges associated with applying AI to MEC security and privacy as possible future research directions.