"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive
  Jammers in IoT Networks

Hoang, DT; Nguyen, DN; Alsheikh, MA; Gong, S; Dutkiewicz, E; Niyato, D; Han, Z

"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks

Hoang, DT Nguyen, DN Alsheikh, MA Gong, S Dutkiewicz, E Niyato, D Han, Z

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Wireless Communications Magazine, 2020
Issue Date:: 2020-05-20

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Field	Value	Language
dc.contributor.author	Hoang, DT
dc.contributor.author	Nguyen, DN
dc.contributor.author	Alsheikh, MA
dc.contributor.author	Gong, S
dc.contributor.author	Dutkiewicz, E
dc.contributor.author	Niyato, D
dc.contributor.author	Han, Z
dc.date.accessioned	2020-06-10T04:34:47Z
dc.date.available	2020-06-10T04:34:47Z
dc.date.issued	2020-05-20
dc.identifier.citation	IEEE Wireless Communications Magazine, 2020
dc.identifier.issn	1070-9916
dc.identifier.uri	http://hdl.handle.net/10453/141229
dc.description.abstract	In this article, we introduce a novel deception strategy which is inspired by the "Borrowing Arrows with Thatched Boats", one of the most famous military tactics in the history, in order to defeat reactive jamming attacks for low-power IoT networks. Our proposed strategy allows resource-constrained IoT devices to be able to defeat powerful reactive jammers by leveraging their own jamming signals. More specifically, by stimulating the jammer to attack the channel through transmitting fake transmissions, the IoT system can not only undermine the jammer's power, but also harvest energy or utilize jamming signals as a communication means to transmit data through using RF energy harvesting and ambient backscatter techniques, respectively. Furthermore, we develop a low-cost deep reinforcement learning framework that enables the hardware-constrained IoT device to quickly obtain an optimal defense policy without requiring any information about the jammer in advance. Simulation results reveal that our proposed framework can not only be very effective in defeating reactive jamming attacks, but also leverage jammer's power to enhance system performance for the IoT network.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Wireless Communications Magazine
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	"Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks
dc.type	Journal Article
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	true
dc.date.updated	2020-06-10T04:34:43Z
pubs.publication-status	Published online

Abstract:

In this article, we introduce a novel deception strategy which is inspired by the "Borrowing Arrows with Thatched Boats", one of the most famous military tactics in the history, in order to defeat reactive jamming attacks for low-power IoT networks. Our proposed strategy allows resource-constrained IoT devices to be able to defeat powerful reactive jammers by leveraging their own jamming signals. More specifically, by stimulating the jammer to attack the channel through transmitting fake transmissions, the IoT system can not only undermine the jammer's power, but also harvest energy or utilize jamming signals as a communication means to transmit data through using RF energy harvesting and ambient backscatter techniques, respectively. Furthermore, we develop a low-cost deep reinforcement learning framework that enables the hardware-constrained IoT device to quickly obtain an optimal defense policy without requiring any information about the jammer in advance. Simulation results reveal that our proposed framework can not only be very effective in defeating reactive jamming attacks, but also leverage jammer's power to enhance system performance for the IoT network.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/141229