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A comprehensive comparison study of ML models for multistage APT detection: focus on data preprocessing and resampling

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Abstract

Advanced persistent threats (APTs) present a significant cybersecurity challenge, necessitating innovative detection methods. This study stands out by integrating advanced data preparation with strategies for handling data imbalances, tailored for the SCVIC-APT-2021 dataset. We employ a mix of resampling, cost-sensitive learning, and ensemble methods, alongside machine learning and deep learning models like XGBoost, LightGBM, and ANNs, to enhance APT detection. Our strategy, which draws from the MITRE ATT&CK framework, concentrates on each stage of APT attacks, which significantly increases detection accuracy. Notably, we achieved a Macro F1-score of 95.20% with XGBoost and 96.67% with LightGBM, and significant enhancements in the area under the precision–recall curve for both. Our study’s exploration of the SCVIC-APT-2021 dataset marks a progressive step in APT detection research, with vital implications for future cybersecurity developments.

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Availability of data and materials

Data generated or analyzed during this study are included in this published article.

Code availability

The custom code developed for the experiments in this study is available upon request from the corresponding author.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Korea National Defense University, Nonsan-si, Chungcheongnam-Do, South Korea

    Dinh-Dong Dau, Soojin Lee & Hanseok Kim

Authors
  1. Dinh-Dong Dau
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  2. Soojin Lee
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Contributions

The primary contributor to the research was Dinh-Dong Dau, who created the test code and edited the research paper’s main content. Hanseok Kim assisted in reviewing language and grammar errors in the research paper. Professor Soojin Lee supervised the research and provided oversight for the entire content of the research paper. All authors read and approved the final manuscript.

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Correspondence to Soojin Lee.

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Dau, DD., Lee, S. & Kim, H. A comprehensive comparison study of ML models for multistage APT detection: focus on data preprocessing and resampling. J Supercomput 80, 14143–14179 (2024). https://doi.org/10.1007/s11227-024-06010-2

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