Detection and Classification of Smart Jamming in Wi-Fi Networks Using Machine Learning

Zhengguang Zhang, Marwan Krunz

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


Smart adversaries can exploit the publicly known frame structure of OFDM-based Wi-Fi protocols to disrupt communications by strategically jamming specific time samples or specific subcarriers. Such attacks are very difficult to detect by traditional techniques like spectral analysis and signal strength indicators. Machine learning (ML) based methods have been proposed to tackle this problem. However, existing ML methods are computationally intensive and perform well only at low signal-to-jamming power ratios (SJRs). In this paper, we propose a computationally efficient deep convolutional neural network (DCNN) consisting of only four convolution layers to detect and classify several smart jamming attacks in Wi-Fi networks. To deal with the time-frequency selectivity of smart jamming, we apply the continuous wavelet transform (CWT) to partially overlapped segments of the received I/Q samples to extract features. The scalogram of the CWT is used as input to the DCNN. We focus on three smart jamming attacks: preamble jamming, pilot jamming, and interleaving jamming. These attacks share similar characteristics, making their differentiation particularly challenging. Our proposed classifier achieves high accuracy in detecting and classifying these jamming attacks across a range of SJRs, from -6 dB to 15 dB, with an overall classification accuracy of 98%. Even at high SJR levels, the accuracy remains high at around 90%. We also train the classifier to be robust against partial preamble jamming and pilot jamming, The resulting classification accuracy is over 90% at SJRs up to 12 dB. Additionally, we compare our classifier with one that uses the spectrogram (short-time Fourier transform) as input to the DCNN, and demonstrate the superior performance of the proposed scalogram-based classifier, particularly in the high SJR regime.

Original languageEnglish (US)
Title of host publicationMILCOM 2023 - 2023 IEEE Military Communications Conference
Subtitle of host publicationCommunications Supporting Military Operations in a Contested Environment
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)9798350321814
StatePublished - 2023
Externally publishedYes
Event2023 IEEE Military Communications Conference, MILCOM 2023 - Boston, United States
Duration: Oct 30 2023Nov 3 2023

Publication series

NameMILCOM 2023 - 2023 IEEE Military Communications Conference: Communications Supporting Military Operations in a Contested Environment


Conference2023 IEEE Military Communications Conference, MILCOM 2023
Country/TerritoryUnited States


  • Deep Neural Networks
  • Smart Jamming classification
  • Wavelet analysis
  • Wi-Fi networks
  • Wireless security

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Signal Processing
  • Safety, Risk, Reliability and Quality


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