Enhancing sniffing detection in IoT home Wi-Fi networks: an ensemble learning approach with Network Monitoring System (NMS)

Hyo Jung Jin, Farshad Rahimi Ghashghaei, Nebrase Elmrabit*, Yussuf Ahmed, Mehdi Yousefi

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Network packet sniffing is one of the techniques that is widely used in the network and cyber security fields. However, sniffing can also be used as a malicious technique that allows threat actors to intercept and capture data flow to collect various information within the victim network. Where the wireless network environment can be vulnerable to sniffing vulnerabilities attacks due to the broadcasting function of Wi-Fi network. Wi-Fi access point devices can often be compromised, and critical information is leaked through sniffing attacks. Moreover, since sniffing is usually one of passive attacks, it is very challenging to detect sniffing activity in the network completely. The primary aim of this research is to contribute to enhancing the security of Internet of Things (IoT) home Wi-Fi systems. This is achieved by applying ensemble machine learning technology with sniffing detection methods using a Network Monitoring System (NMS) to effectively identify and mitigate potential sniffing behaviour within the IoT home Wi-Fi environment. Ultimately, this research will prove whether it is possible to precisely detect abnormal sniffing in a smart home Wi-Fi environment using machine learning techniques.
Original languageEnglish
Pages (from-to)86840-86853
Number of pages14
JournalIEEE Access
Volume12
Early online date18 Jun 2024
DOIs
Publication statusE-pub ahead of print - 18 Jun 2024

Keywords

  • ensemble learning
  • network monitoring system (NMS)
  • smart home
  • sniffing
  • Wi-Fi
  • sociology
  • machine learning
  • wireless fidelity
  • telecommunication traffic
  • internet of things
  • monitoring

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering

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