Estimating Traffic Rates in CSMA/CA Networks: A Feasibility Analysis for a Class of Eavesdroppers

Yirong Cheng, Eric Graves, Ananthram Swami, Ashutosh Sabharwal

Research output: Contribution to journalArticlepeer-review

Abstract

Estimation of transmission rates by a malicious user can serve as a stepping stone to further attacks on the network. In this paper, we aim to investigate the general problem of estimating traffic transmission rates in a CSMA/CA network using a class of passive eavesdropping methods. We consider the case where a single eavesdropper passively monitors all active network nodes but cannot observe all packet transmissions due to spatial-reuse collisions. To enable tractable analysis, we first propose an approximate statistical model that can help the eavesdropper estimate transmission rates with partial measurements under spatial reuse. We next consider a class of eavesdroppers that become increasingly more capable, and develop a framework to demonstrate that two classes of eavesdropper capabilities are sufficient to achieve a consistent transmission rate estimator. We provide numerical tests of our proposed estimators under practical network cases using the NS-3 simulator that validate the theoretical results.

Original languageEnglish (US)
Pages (from-to)9793-9807
Number of pages15
JournalIEEE Transactions on Wireless Communications
Volume22
Issue number12
DOIs
StatePublished - Dec 1 2023

Keywords

  • CSMA/CA
  • Ergodic theorem
  • IEEE80211
  • Internet of Things (IoT)
  • RF fingerprinting
  • eavesdropping
  • network spatial reuse
  • statistical traffic analysis
  • stochastic process
  • wireless security

ASJC Scopus subject areas

  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Applied Mathematics

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