TY - JOUR
T1 - "Borrowing Arrows with Thatched Boats": The Art of Defeating Reactive Jammers in IoT Networks
AU - Hoang, Dinh Thai
AU - Nguyen, Diep N.
AU - Alsheikh, Mohammad Abu
AU - Gong, Shimin
AU - Dutkiewicz, Eryk
AU - Niyato, Dusit
AU - Han, Zhu
N1 - Funding Information:
This work was supported in part by the Australian Research Council (ARC) under grant DE200100863. This work was supported in part by US MURI AFOSR MURI 18RT0073, NSF EARS-1839818, CNS-1717454, CNS-1731424, and CNS-1702850. This work was supported in part by Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A\u2217STAR-NTU-SUTD Joint Research Grant Call on Artifi cial Intelligence for the Future of Manufacturing RGANS1906, WASP/NTU M4082187 (4080), Singapore MOE Tier 1 2017-T1-002-007 RG122/17, MOE Tier 2 MOE2014-T2-2-015 ARC4/15, Singapore NRF2015-NRF-ISF001-2277, and Singapore EMA Energy Resilience NRF2017EWT-EP003-041. The work of Shimin Gong was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61972434, the Shenzhen Basic Research Program under Grant JCYJ20190807154009444, the Shenzhen Talent Peacock Plan Program under Grant KQTD2015071715073798, and the PCL Future Greater-Bay Area Network Facilities for Largescale Experiments and Applications (LZC0019).
Funding Information:
Acknowledgments This work was supported in part by the Australian Research Council (ARC) under grant DE200100863. This work was supported in part by US MURI AFOSR MURI 18RT0073, NSF EARS-1839818, CNS-1717454, CNS-1731424, and CNS-1702850. This work was supported in part by Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A*STAR-NTU-SUTD Joint Research Grant Call on Artificial Intelligence for the Future of Manufacturing RGANS1906, WASP/NTU M4082187 (4080), Singapore MOE Tier 1 2017-T1-002-007 RG122/17, MOE Tier 2 MOE2014-T2-2-015 ARC4/15, Singapore NRF2015-NRF-ISF001-2277, and Singapore EMA Energy Resilience NRF2017EWT-EP003-041. The work of Shimin Gong was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 61972434, the Shenzhen Basic Research Program under Grant JCYJ20190807154009444, the Shenzhen Talent Peacock Plan Program under Grant KQTD2015071715073798, and the PCL Future Greater-Bay Area Network Facilities for Lar-gescale Experiments and Applications (LZC0019).
Publisher Copyright:
© 2002-2012 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - In this article, we introduce a novel deception strategy inspired by the "Borrowing Arrows with Thatched Boats" strategy, one of the most famous military tactics in 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 a jammer's power to enhance system performance for the IoT network.
AB - In this article, we introduce a novel deception strategy inspired by the "Borrowing Arrows with Thatched Boats" strategy, one of the most famous military tactics in 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 a jammer's power to enhance system performance for the IoT network.
KW - cybersecurity
KW - Internet of things
UR - http://www.scopus.com/inward/record.url?scp=85086888364&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/c54351bd-ac73-38ee-8483-a48b935fc635/
U2 - 10.1109/MWC.001.1900451
DO - 10.1109/MWC.001.1900451
M3 - Article
AN - SCOPUS:85086888364
SN - 1536-1284
VL - 27
SP - 79
EP - 87
JO - IEEE Wireless Communications
JF - IEEE Wireless Communications
IS - 3
ER -