A CatBoost-Based Approach for High-Accuracy Botnet Detection

Abdulkader Hajjouz ()

Technium, 2023, vol. 15, issue 1, 26-32

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.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:tec:techni:v:15:y:2023:i:1:p:26-32

DOI: 10.47577/technium.v15i.9635

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