Secure Edge Computing in IoT via Online Learning

Bingcong Li, Tianyi Chen, Xin Wang, Georgios B Giannakis

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

Abstract

To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges, extending the computing service from the cloud to edge, but at the same time exposing new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, we develop SAVE-S algorithm that is tailored for the stochastic jamming. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior knowledge of future jamming information and server security risks, the proposed scheme can achieve mathrm{O}(sqrt{T}) regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S has significant improvements on the sublinear regret, which is guaranteed by what we term value of cooperation. The effectiveness of proposed schemes are tested on both synthetic and real datasets.

Original languageEnglish (US)
Title of host publicationConference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages2149-2153
Number of pages5
ISBN (Electronic)9781538692189
DOIs
StatePublished - Feb 19 2019
Event52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 - Pacific Grove, United States
Duration: Oct 28 2018Oct 31 2018

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2018-October
ISSN (Print)1058-6393

Conference

Conference52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018
CountryUnited States
CityPacific Grove
Period10/28/1810/31/18

Fingerprint

Jamming
Servers
Power spectrum
Power transmission
Internet of things
Communication

Keywords

  • Cyber security
  • jamming attack
  • mobile edge computing
  • multi-armed bandit
  • online learning

Cite this

Li, B., Chen, T., Wang, X., & Giannakis, G. B. (2019). Secure Edge Computing in IoT via Online Learning. In M. B. Matthews (Ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018 (pp. 2149-2153). [8645223] (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October). IEEE Computer Society. https://doi.org/10.1109/ACSSC.2018.8645223

Secure Edge Computing in IoT via Online Learning. / Li, Bingcong; Chen, Tianyi; Wang, Xin; Giannakis, Georgios B.

Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. ed. / Michael B. Matthews. IEEE Computer Society, 2019. p. 2149-2153 8645223 (Conference Record - Asilomar Conference on Signals, Systems and Computers; Vol. 2018-October).

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

Li, B, Chen, T, Wang, X & Giannakis, GB 2019, Secure Edge Computing in IoT via Online Learning. in MB Matthews (ed.), Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018., 8645223, Conference Record - Asilomar Conference on Signals, Systems and Computers, vol. 2018-October, IEEE Computer Society, pp. 2149-2153, 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018, Pacific Grove, United States, 10/28/18. https://doi.org/10.1109/ACSSC.2018.8645223
Li B, Chen T, Wang X, Giannakis GB. Secure Edge Computing in IoT via Online Learning. In Matthews MB, editor, Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. IEEE Computer Society. 2019. p. 2149-2153. 8645223. (Conference Record - Asilomar Conference on Signals, Systems and Computers). https://doi.org/10.1109/ACSSC.2018.8645223
Li, Bingcong ; Chen, Tianyi ; Wang, Xin ; Giannakis, Georgios B. / Secure Edge Computing in IoT via Online Learning. Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018. editor / Michael B. Matthews. IEEE Computer Society, 2019. pp. 2149-2153 (Conference Record - Asilomar Conference on Signals, Systems and Computers).
@inproceedings{9ae9b411d91a49f7a5352c950e3b1900,
title = "Secure Edge Computing in IoT via Online Learning",
abstract = "To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges, extending the computing service from the cloud to edge, but at the same time exposing new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, we develop SAVE-S algorithm that is tailored for the stochastic jamming. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior knowledge of future jamming information and server security risks, the proposed scheme can achieve mathrm{O}(sqrt{T}) regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S has significant improvements on the sublinear regret, which is guaranteed by what we term value of cooperation. The effectiveness of proposed schemes are tested on both synthetic and real datasets.",
keywords = "Cyber security, jamming attack, mobile edge computing, multi-armed bandit, online learning",
author = "Bingcong Li and Tianyi Chen and Xin Wang and Giannakis, {Georgios B}",
year = "2019",
month = "2",
day = "19",
doi = "10.1109/ACSSC.2018.8645223",
language = "English (US)",
series = "Conference Record - Asilomar Conference on Signals, Systems and Computers",
publisher = "IEEE Computer Society",
pages = "2149--2153",
editor = "Matthews, {Michael B.}",
booktitle = "Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018",

}

TY - GEN

T1 - Secure Edge Computing in IoT via Online Learning

AU - Li, Bingcong

AU - Chen, Tianyi

AU - Wang, Xin

AU - Giannakis, Georgios B

PY - 2019/2/19

Y1 - 2019/2/19

N2 - To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges, extending the computing service from the cloud to edge, but at the same time exposing new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, we develop SAVE-S algorithm that is tailored for the stochastic jamming. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior knowledge of future jamming information and server security risks, the proposed scheme can achieve mathrm{O}(sqrt{T}) regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S has significant improvements on the sublinear regret, which is guaranteed by what we term value of cooperation. The effectiveness of proposed schemes are tested on both synthetic and real datasets.

AB - To accommodate heterogeneous tasks in Internet of Things (IoT), a new communication and computing paradigm termed mobile edge computing emerges, extending the computing service from the cloud to edge, but at the same time exposing new challenges on security. The present paper studies online security-aware edge computing under jamming attacks. Leveraging online learning tools, we develop SAVE-S algorithm that is tailored for the stochastic jamming. Without utilizing extra resources such as spectrum and transmission power to evade jamming attacks, SAVE-S can select the most reliable server to offload computing tasks with minimal privacy and security concerns. It is analytically established that without any prior knowledge of future jamming information and server security risks, the proposed scheme can achieve mathrm{O}(sqrt{T}) regret. Information sharing among devices can accelerate the security-aware computing tasks. Incorporating the information shared by other devices, SAVE-S has significant improvements on the sublinear regret, which is guaranteed by what we term value of cooperation. The effectiveness of proposed schemes are tested on both synthetic and real datasets.

KW - Cyber security

KW - jamming attack

KW - mobile edge computing

KW - multi-armed bandit

KW - online learning

UR - http://www.scopus.com/inward/record.url?scp=85062955504&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062955504&partnerID=8YFLogxK

U2 - 10.1109/ACSSC.2018.8645223

DO - 10.1109/ACSSC.2018.8645223

M3 - Conference contribution

AN - SCOPUS:85062955504

T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers

SP - 2149

EP - 2153

BT - Conference Record of the 52nd Asilomar Conference on Signals, Systems and Computers, ACSSC 2018

A2 - Matthews, Michael B.

PB - IEEE Computer Society

ER -