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    Unsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networks

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    Accepted manuscript (816.2Kb)
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    Publication date
    2023-01
    Author
    Lefoane, Moemedi
    Ghafir, Ibrahim
    Kabir, Sohag
    Awan, Irfan U.
    Keyword
    Botnet attack
    Internet of Things
    Network security
    Intrusion detection system
    Machine learning
    Feature selection
    Rights
    © 2022 IEEE. Reproduced in accordance with the publisher's self-archiving policy. See https://www.ieee.org/publications/rights/index.html for more information.
    Peer-Reviewed
    Yes
    Open Access status
    openAccess
    
    Metadata
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    Abstract
    The world has seen exponential growth in deploying Internet of Things (IoT) devices. In recent years, connected IoT devices have surpassed the number of connected non-IoT devices. The number of IoT devices continues to grow and they are becoming a critical component of the national infrastructure. IoT devices' characteristics and inherent limitations make them attractive targets for hackers and cyber criminals. Botnet attack is one of the serious threats on the Internet today. This article proposes pattern-based feature selection methods as part of a machine learning (ML) based botnet detection system. Specifically, two methods are proposed: the first is based on the most dominant pattern feature values and the second is based on Maximal Frequent Itemset (MFI) mining. The proposed feature selection method uses Gini Impurity (GI) and an unsupervised clustering method to select the most influential features automatically. The evaluation results show that the proposed methods have improved the performance of the detection system. The developed system has a True Positive Rate (TPR) of 100% and a False Positive Rate (FPR) of 0% for best performing models. In addition, the proposed methods reduce the computational cost of the system as evidenced by the detection speed of the system.
    URI
    http://hdl.handle.net/10454/19101
    Version
    Accepted manuscript
    Citation
    Lefoane M, Ghafir I, Kabir S et al (2022) Unsupervised Learning for Feature Selection: A Proposed Solution for Botnet Detection in 5G Networks. IEEE Transactions on Industrial Informatics. 19(1): 921-929.
    Link to publisher’s version
    https://doi.org/10.1109/TII.2022.3192044
    Type
    Article
    Collections
    Engineering and Informatics Publications

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