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A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets

Author

Listed:
  • Ranjit Panigrahi

    (Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India
    These authors contributed equally to this work and are first co-authors.)

  • Samarjeet Borah

    (Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India)

  • Akash Kumar Bhoi

    (Department of Electrical and Electronics Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India)

  • Muhammad Fazal Ijaz

    (Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
    These authors contributed equally to this work and are first co-authors.)

  • Moumita Pramanik

    (Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Majitar 737136, Sikkim, India)

  • Yogesh Kumar

    (Department of Computer Science and Engineering, Chandigarh Group of Colleges, Landran 140301, Punjab, India)

  • Rutvij H. Jhaveri

    (Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, Gujarat, India)

Abstract

The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.

Suggested Citation

  • Ranjit Panigrahi & Samarjeet Borah & Akash Kumar Bhoi & Muhammad Fazal Ijaz & Moumita Pramanik & Yogesh Kumar & Rutvij H. Jhaveri, 2021. "A Consolidated Decision Tree-Based Intrusion Detection System for Binary and Multiclass Imbalanced Datasets," Mathematics, MDPI, vol. 9(7), pages 1-35, March.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:7:p:751-:d:527525
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    References listed on IDEAS

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    1. Hamed Taherdoost, 2016. "Sampling Methods in Research Methodology; How to Choose a Sampling Technique for Research," Post-Print hal-02546796, HAL.
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    Cited by:

    1. Jani Dugonik & Mirjam Sepesy Maučec & Domen Verber & Janez Brest, 2023. "Reduction of Neural Machine Translation Failures by Incorporating Statistical Machine Translation," Mathematics, MDPI, vol. 11(11), pages 1-22, May.
    2. P. Chellammal & Sheba Kezia Malarchelvi & K. Reka & G. Raja, 2022. "Fast and Effective Intrusion Detection Using Multi-Layered Deep Learning Networks," International Journal of Web Services Research (IJWSR), IGI Global, vol. 19(1), pages 1-16, January.

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