Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS)
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...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10559972/ |
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| author | Hyo Jung Jin Farshad Rahimi Ghashghaei Nebrase Elmrabit Yussuf Ahmed Mehdi Yousefi |
| author_facet | Hyo Jung Jin Farshad Rahimi Ghashghaei Nebrase Elmrabit Yussuf Ahmed Mehdi Yousefi |
| author_sort | Hyo Jung Jin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a80b51b0990141ae89da05824bb8d1b6 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-a80b51b0990141ae89da05824bb8d1b62025-08-20T02:52:19ZengIEEEIEEE Access2169-35362024-01-0112868408685310.1109/ACCESS.2024.341609510559972Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS)Hyo Jung Jin0https://orcid.org/0009-0002-7439-755XFarshad Rahimi Ghashghaei1https://orcid.org/0009-0001-9634-3301Nebrase Elmrabit2https://orcid.org/0000-0002-4267-8798Yussuf Ahmed3https://orcid.org/0000-0003-4079-9243Mehdi Yousefi4https://orcid.org/0000-0003-0832-650XSchool of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.Department of Cyber Security and Networks, Glasgow Caledonian University, Glasgow, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.School of Computing and Digital Technology, Birmingham City University, Birmingham, U.K.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.https://ieeexplore.ieee.org/document/10559972/Ensemble learningnetwork monitoring system (NMS)smart homesniffingWi-Fi |
| spellingShingle | Hyo Jung Jin Farshad Rahimi Ghashghaei Nebrase Elmrabit Yussuf Ahmed Mehdi Yousefi Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) IEEE Access Ensemble learning network monitoring system (NMS) smart home sniffing Wi-Fi |
| title | Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) |
| title_full | Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) |
| title_fullStr | Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) |
| title_full_unstemmed | Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) |
| title_short | Enhancing Sniffing Detection in IoT Home Wi-Fi Networks: An Ensemble Learning Approach With Network Monitoring System (NMS) |
| title_sort | enhancing sniffing detection in iot home wi fi networks an ensemble learning approach with network monitoring system nms |
| topic | Ensemble learning network monitoring system (NMS) smart home sniffing Wi-Fi |
| url | https://ieeexplore.ieee.org/document/10559972/ |
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