Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches

Journal article


Yonbawi, S., Afzal, A., Yasir, M., Rizwan, M. and Kryvinska, N. 2025. Transferability Evaluation in Wi-Fi Intrusion Detection Systems Through Machine Learning and Deep Learning Approaches. IEEE Access. 13, pp. 11248 - 11264. https://doi.org/10.1109/ACCESS.2025.3528214
AuthorsYonbawi, S., Afzal, A., Yasir, M., Rizwan, M. and Kryvinska, N.
Abstract

Intrusion Detection System (IDS) plays a pivotal role in safeguarding network security. The efficacy of these systems is rigorously assessed through established metrics including precision, recall, F1 score, and AUC score. When subjected to rigorous testing on well-known datasets like AWID and AWID3, individual IDS models consistently deliver exceptional performances, boasting F1 scores ranging from 0.98 to 1 and AUC scores spanning 0.97 to 0.99. However, the true challenge surfaces when the objective is to extend the transferability of these high-performing models to entirely novel, unseen datasets. This endeavor unravels a diverse performance landscape, demonstrating that the outstanding performance observed on a particular dataset doesn’t guarantee the transferability of features across dissimilar datasets nestled within different network environments. In order to evaluate the feature transferability, we turn to AWID and AWID3 datasets as the main distinction between AWID (potentially referring to AWID2) and AWID3 lies in their specific focuses and contexts within the field of Wi-Fi intrusion detection. Although both datasets are centered on the general goal of detecting Wi-Fi intrusions, AWID3 has been carefully designed to meet the specific needs of corporate Wi-Fi applications. A comprehensive evaluation involving Multilayer Perceptron(MLP), and Convolutional Neural Networks (CNN) models has been executed, uncovering that CNN conspicuously outshines the MLP model.

KeywordsTransferability assessment; performance evaluation; intrusion detection system (IDS); deep learning; wireless security
Year2025
JournalIEEE Access
Journal citation13, pp. 11248 - 11264
PublisherIEEE
ISSN2169-3536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2025.3528214
Web address (URL)https://ieeexplore.ieee.org/document/10836233
Accepted author manuscript
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Open
Publisher's version
License
File Access Level
Open
Output statusPublished
Publication dates
Online10 Jan 2025
Publication process dates
Accepted30 Dec 2024
Deposited11 Feb 2025
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https://repository.derby.ac.uk/item/qw215/transferability-evaluation-in-wi-fi-intrusion-detection-systems-through-machine-learning-and-deep-learning-approaches

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File access level: Open

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