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    DDOS attacks detection based on attention-deep learning and local outlier factor

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    DDOS_attacks_detection_based_on_attention_deep_learning_and_local_outlier_factor.pdf
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    Type
    Conference Paper
    Authors
    Dairi, Abdelkader
    Khaldi, Belkacem
    Harrou, Fouzi cc
    Sun, Ying cc
    KAUST Department
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Statistics Program
    Date
    2023-03-14
    Permanent link to this record
    http://hdl.handle.net/10754/690392
    
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    Abstract
    One of the most significant security concerns confronting network technology is the detection of distributed denial of service (DDOS). This paper introduces a semi-supervised data-driven approach to the detection of DDOS attacks. The proposed method employs normal events data without labeling to train the detection model. Specifically, this approach introduces an improved autoencoder (AE) model by incorporating a Gated Recurrent Unit (GRU) based on the attention mechanism (AM) at the encoder and decoder sides of the AE model. GRU enhances the AE's ability to learn temporal dependencies, and the AM enables the selection of relevant features. For DDOS attacks detection, the local outlier factor (LOF) anomaly detection algorithm is applied to extracted features from the improved AE model. The performance of the proposed approach has been verified using DDOS publically available datasets.
    Citation
    Dairi, A., Khaldi, B., Harrou, F., & Sun, Y. (2022). DDOS attacks detection based on attention-deep learning and local outlier factor. 2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC). https://doi.org/10.1109/fmec57183.2022.10062705
    Publisher
    IEEE
    Conference/Event name
    2022 Seventh International Conference on Fog and Mobile Edge Computing (FMEC)
    DOI
    10.1109/fmec57183.2022.10062705
    Additional Links
    https://ieeexplore.ieee.org/document/10062705/
    ae974a485f413a2113503eed53cd6c53
    10.1109/fmec57183.2022.10062705
    Scopus Count
    Collections
    Conference Papers; Statistics Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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