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Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
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      02 June 2023, Volume 38 Issue 3
    Article

    COMPARATIVE ANALYSIS OF RESAMPLING TECHNIQUES ON IMBALANCED CIE-CICIDS2018 DATASET FOR DOS ATTACK DETECTION
    Supriya Dicholkar, Jagannath Nirmal
    Journal of Data Acquisition and Processing, 2023, 38 (3): 2249-2260 . 

    Abstract

    The Internet of Things (IoT) is a largely emerging area having applications in almost all sectors but a threat to the security of the IoT network is the main hurdle in the growth of IoT networks. For attack detection, standard IoT datasets are used. These datasets are highly imbalanced with major benign traffic and very little attack traffic. To deal with the imbalanced dataset in this paper, different resampling techniques such as Undersampling, Oversampling, and hybrid sampling are applied to the CIE-CICIDS2018 dataset. After resampling, the artificial neural network is applied for attack detection on this resampled dataset. As the dataset is imbalanced, for evaluation of the performance along with accuracy, precision, recall, and the F1 score are parameters used. Random Undersampling is giving the best result among all resampling techniques but a lot of data loss occurred in Random Undersampling. Edited Nearest neighbors is giving better results than all other techniques except Random Undersampling without losing the majority of data samples.

    Keyword

    class imbalance,CICIDS2018, IDS, resampling,


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ISSN 1004-9037

         

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