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Studying the Attack Detection Problem Using the Dataset CIDDS-001

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Digital Science (DSIC 2021)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 381))

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Abstract

Intrusion detection is an important problem in cybersecurity research. In recent years, researchers have leveraged different machine learning algorithms to empower intrusion detection systems (IDS). In this paper, we study the intrusion detection problem using the dataset CIDDS-001 released in 2017. The dataset is much different from the popular datasets using in the literature in that it is not equipped with a comprehensive feature list. We show empirically that we can effectively classify the attacks by using state-of-the-art machine learning algorithms.

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Authors and Affiliations

  1. Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam

    Quang-Vinh Dang

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  1. Quang-Vinh Dang
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Correspondence to Quang-Vinh Dang .

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Editors and Affiliations

  1. Institute of Certified Specialists, Perm, Russia

    Tatiana Antipova

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Dang, QV. (2022). Studying the Attack Detection Problem Using the Dataset CIDDS-001. In: Antipova, T. (eds) Digital Science. DSIC 2021. Lecture Notes in Networks and Systems, vol 381. Springer, Cham. https://doi.org/10.1007/978-3-030-93677-8_46

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