An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks.
The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Exis...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2025-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0306747 |
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| _version_ | 1841555581281763328 |
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| author | Nashmia Khalid Sadaf Hina Khurram Shabih Zaidi Tarek Gaber Lee Speakman Zainab Noor |
| author_facet | Nashmia Khalid Sadaf Hina Khurram Shabih Zaidi Tarek Gaber Lee Speakman Zainab Noor |
| author_sort | Nashmia Khalid |
| collection | DOAJ |
| description | The widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retained features were analyzed to assess their transferability, creating a lightweight and cost-effective model. A decision tree with a recursive feature elimination method was implemented for the extraction of the reduced features subset, and an additional feature wlan_radio.signal_dbm was used in combination with the extracted feature subset. Several deep learning and machine learning models were implemented, where DT and CNN achieved promising classification results. Further, feature transferability and generalizability were evaluated, and their detection performance was analyzed across different network versions where CNN outperformed other classification models. The practical implications of this research are crucial for the secure automation of wireless intrusion detection frameworks and tools in personal and enterprise paradigms. |
| format | Article |
| id | doaj-art-1461fd9b31334c0894e9d8463d5266a1 |
| institution | Kabale University |
| issn | 1932-6203 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-1461fd9b31334c0894e9d8463d5266a12025-01-08T05:31:54ZengPublic Library of Science (PLoS)PLoS ONE1932-62032025-01-01201e030674710.1371/journal.pone.0306747An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks.Nashmia KhalidSadaf HinaKhurram Shabih ZaidiTarek GaberLee SpeakmanZainab NoorThe widespread use of wireless networks to transfer an enormous amount of sensitive information has caused a plethora of vulnerabilities and privacy issues. The management frames, particularly authentication and association frames, are vulnerable to cyberattacks and it is a significant concern. Existing research in Wi-Fi attack detection focused on obtaining high detection accuracy while neglecting modern traffic and attack scenarios such as key reinstallation or unauthorized decryption attacks. This study proposed a novel approach using the AWID 3 dataset for cyberattack detection. The retained features were analyzed to assess their transferability, creating a lightweight and cost-effective model. A decision tree with a recursive feature elimination method was implemented for the extraction of the reduced features subset, and an additional feature wlan_radio.signal_dbm was used in combination with the extracted feature subset. Several deep learning and machine learning models were implemented, where DT and CNN achieved promising classification results. Further, feature transferability and generalizability were evaluated, and their detection performance was analyzed across different network versions where CNN outperformed other classification models. The practical implications of this research are crucial for the secure automation of wireless intrusion detection frameworks and tools in personal and enterprise paradigms.https://doi.org/10.1371/journal.pone.0306747 |
| spellingShingle | Nashmia Khalid Sadaf Hina Khurram Shabih Zaidi Tarek Gaber Lee Speakman Zainab Noor An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. PLoS ONE |
| title | An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. |
| title_full | An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. |
| title_fullStr | An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. |
| title_full_unstemmed | An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. |
| title_short | An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. |
| title_sort | investigation of feature reduction transferability and generalization in awid datasets for secure wi fi networks |
| url | https://doi.org/10.1371/journal.pone.0306747 |
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