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Darknet traffic analysis, and classification system based on modified stacking ensemble learning algorithms

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  • Ammar Almomani

    (Al-Huson University College, Al-Balqa Applied University
    Skyline University College)

Abstract

Darknet, a source of cyber intelligence, refers to the internet’s unused address space, which people do not expect to interact with their computers. The establishment of security requires analyses of the threats characterizing the network. New machine learning classifiers known as stacking ensemble learning are proposed in this paper to analyze and classify darknet traffic. In dealing with darknet attack problems, this new system uses predictions formed by 3 base learning techniques. The system was tested on a dataset comprising more than 141,000 records analyzed from CIC-Darknet 2020. The experiment results demonstrated the study’s classifiers’ ability to distinguish between the malignant traffic and benign traffic easily. The classifiers can effectively detect known and unknown threats with high precision and accuracy greater than 99% in the training and 97% in the testing phases, with increments ranging from 4 to 64% by current algorithms. As a result, the proposed system becomes more robust and accurate as data grows. Also, the proposed system has the best standard deviation compared with current A.I. algorithms.

Suggested Citation

  • Ammar Almomani, 2025. "Darknet traffic analysis, and classification system based on modified stacking ensemble learning algorithms," Information Systems and e-Business Management, Springer, vol. 23(1), pages 209-240, March.
    • Handle: RePEc:spr:infsem:v:23:y:2025:i:1:d:10.1007_s10257-023-00626-2
    DOI: 10.1007/s10257-023-00626-2
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    References listed on IDEAS

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    1. Anupama Mishra & Neena Gupta & B. B. Gupta, 2021. "Defense mechanisms against DDoS attack based on entropy in SDN-cloud using POX controller," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 47-62, May.
    2. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
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