Secure Mobile Edge Computing in IoT via Collaborative Online Learning

TL;DR

This paper introduces online learning algorithms for secure mobile edge computing in IoT, effectively mitigating jamming attacks without extra resource expenditure, and demonstrates their theoretical and practical benefits.

Contribution

It develops novel online algorithms, SAVE-S and SAVE-A, for security-aware edge computing under stochastic and adversarial jamming, with proven regret bounds and cooperative enhancements.

Findings

Algorithms achieve ${\cal O}(\sqrt{T})$ regret without prior jamming information.

Sharing information among devices improves security and reduces regret.

Proven effectiveness on synthetic and real datasets.

Abstract

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges that extends computing services from the cloud to edge, but at the same time exposes new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, novel algorithms abbreviated as SAVE-S and SAVE-A are developed to cope with the stochastic and adversarial forms of jamming, respectively. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S and SAVE-A can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior information on future jamming and server security risks, the proposed schemes can achieve ${\cal O}\big(\sqrt{T}\big)$ regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S and SAVE-A offer impressive improvements on the sublinear regret, which is guaranteed by what is termed "value of cooperation." Effectiveness of the proposed schemes is tested on both synthetic and real datasets.