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A CatBoost-Based Approach for High-Accuracy Botnet Detection

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  • Abdulkader Hajjouz

Abstract

The rising prevalence of network botnet attacks poses a significant threat to online security. Compromised networks controlled by malicious entities can perpetrate harm, including distributed denial of service attacks and data theft. In this study, we introduce a method to detect these botnets using the CatBoostClassifier. By analyzing network traffic for suspicious patterns, our system efficiently identifies potential botnet activities. Utilizing the CTU-13 dataset, we achieved an impressive 99.8699% accuracy, underscoring the efficacy of our approach. This research offers valuable insights into botnet attack detection and presents a robust solution for enhancing network security.

Suggested Citation

  • Abdulkader Hajjouz, 2023. "A CatBoost-Based Approach for High-Accuracy Botnet Detection," Technium, Technium Science, vol. 15(1), pages 26-32.
    • Handle: RePEc:tec:techni:v:15:y:2023:i:1:p:26-32
    DOI: 10.47577/technium.v15i.9635
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    References listed on IDEAS

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    1. Sheeraz Ahmed & Zahoor Ali Khan & Syed Muhammad Mohsin & Shahid Latif & Sheraz Aslam & Hana Mujlid & Muhammad Adil & Zeeshan Najam, 2023. "Effective and Efficient DDoS Attack Detection Using Deep Learning Algorithm, Multi-Layer Perceptron," Future Internet, MDPI, vol. 15(2), pages 1-24, February.
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      JEL classification:

      • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
      • Z0 - Other Special Topics - - General

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