Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS)

Jung Jin, Hyo and Rahimi Ghashghaei, Farshad and Elmrabit, Nebrase and Ahmed, Yussuf and Yousefi, Mehdi (2024) Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS). IEEE Access, 12. pp. 86840-86853. ISSN 2169-3536

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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.

Item Type: Article
Identification Number: 10.1109/ACCESS.2024.3416095
Dates:
Date
Event
1 June 2024
Accepted
18 June 2024
Published Online
Uncontrolled Keywords: Ensemble learning, network monitoring system (NMS), smart home, sniffing, Wi-Fi
Subjects: CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions: Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User: Gemma Tonks
Date Deposited: 12 Aug 2024 15:01
Last Modified: 12 Aug 2024 15:01
URI: https://www.open-access.bcu.ac.uk/id/eprint/15710

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