Fooling Edge Computation Offloading via Stealthy Interference Attack

Letian Zhang, Jie Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

There is a growing interest in developing deep learning methods to solve many resource management problems in wireless edge computing systems where model-based designs are infeasible. While deep learning is known to be vulnerable to adversarial example attacks, the security risk of learningbased designs in the context of edge computing is not well understood. In this paper, we propose and study a new adversarial example attack, called stealthy interference attack (SIA), in deep reinforcement learning (DRL)-based edge computation offloading systems. In SIA, the attacker exerts a carefully determined level of interference signal to change the input states of the DRL-based policy, thereby fooling the mobile device in selecting a target and compromised edge server for computation offloading while evading detection. Simulation results demonstrate the effectiveness of SIA, and show that our algorithm outperforms existing adversarial machine learning algorithms in terms of a higher attack success probability and a lower power consumption.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages415-419
Number of pages5
ISBN (Electronic)9781728159430
DOIs
StatePublished - Nov 2020
Event5th IEEE/ACM Symposium on Edge Computing, SEC 2020 - Virtual, San Jose, United States
Duration: Nov 11 2020Nov 13 2020

Publication series

NameProceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020

Conference

Conference5th IEEE/ACM Symposium on Edge Computing, SEC 2020
Country/TerritoryUnited States
CityVirtual, San Jose
Period11/11/2011/13/20

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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