Deep Learning-Guided Jamming for Cross-Technology Wireless Networks: Attack and Defense

Dianqi Han, Ang Li, Lili Zhang, Yan Zhang, Jiawei Li, Tao Li, Ting Zhu, Yanchao Zhang

Research output: Contribution to journalArticlepeer-review

Abstract

Wireless networks of different technologies may interfere with each other when they are deployed at proximity. Such cross-technology interference (CTI) has become prevalent with the surge of IoT devices. In this paper, we exploit CTI in coexisting WiFi-Zigbee networks and propose DeepJam, a new stealthy jamming strategy, to jam Zigbee traffic. DeepJam relies on deep learning techniques to capture the temporal pattern of the past wireless traffic and predict the future wireless traffic. By only jamming the victim's transmissions that are not disrupted by CTI, DeepJam can significantly reduce the victim's throughput with far fewer jamming signals and is thus much more stealthy than conventional jamming strategies. Detailed evaluations show that DeepJam can converge within 10 sec and achieve the jamming-efficiency gains of up to 742% and 285% over conventional random and reactive jamming strategies, respectively, in practical scenarios. We also propose a simple yet effective countermeasure against DeepJam.

Original languageEnglish (US)
JournalIEEE/ACM Transactions on Networking
DOIs
StateAccepted/In press - 2021

Keywords

  • Communication system security
  • Deep learning
  • Interference
  • Jamming
  • Jamming
  • Throughput
  • WiFi and Zigbee
  • Wireless fidelity
  • Zigbee
  • cross-technology interference
  • reinforcement learning.

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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