An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks

Khalid, Nashmia; Hina, Sadaf; Khurram, Shabih; Zaidi; Gaber, Tarek; Speakman, Lee; Noor, Zainab

Abstract

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.

Citation

Khalid, N., Hina, S., Khurram, S., Zaidi, Gaber, T., Speakman, L., & Noor, Z. (in press). An investigation of feature reduction, transferability, and generalization in AWID datasets for secure Wi-Fi networks. PLoS ONE, 20(1), e0306747. https://doi.org/10.1371/journal.pone.0306747

• Journal Article Type: Article
• Acceptance Date: Jun 21, 2024
• Online Publication Date: Jan 2, 2025
• Deposit Date: Nov 19, 2024
• Publicly Available Date: Jan 2, 2025
• Journal: PLOS ONE
• Electronic ISSN: 1932-6203
• Publisher: Public Library of Science
• Peer Reviewed: Peer Reviewed
• Volume: 20
• Issue: 1
• Pages: e0306747
• DOI: https://doi.org/10.1371/journal.pone.0306747
• Keywords: Feature Transferability, Wireless Communication; Authentication Attacks; Unauthorized Decryption; Machine Learning