Chatterjee, Cheeradeep, George, Shiju, and Jayapandian, N. (2025) Advanced Botnet Detection Using Hybrid Machine Learning Models. In: Proceedings of the IEEE International Conference on Contemporary Computing and Communications. From: InC4 2025: IEEE International Conference on Contemporary Computing and Communications, 14-15 March 2025, Bangalore, India.

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Abstract

The improvement of computer network systems, cyberattacks that take advantage of system flaws have increased, resulting in significant monetary losses, business interruptions, harm to one's reputation, and legal repercussions. This research examines nine attack types, those are Fuzzers, Shellcode, Generic, Worms, Analysis, Normal, DoS, Exploits, Backdoor, and Reconnaissance. Botnet assaults are attacks in which a single operator controls several networked devices. The research study examines several models, such as Random Forest, XGBoost Classifier, Logistic Regression, and Decision Tree, to improve detecting skills. By utilizing the advantages of both methods, the suggested ERFwXGBoost (Enhanced Random Forest with XGBoost) model, which blends Random Forest and XGBoost, exhibits remarkable performance. Notably, first they analyze the accuracy, then precision value is also measured, third will measure recall value, and then finally F1 score of the ERFwXGBoost model are all impressively 0.98. In addition to outperforming individual models, our hybrid technique offers a reliable and effective way to detect different kinds of botnet attacks. The research emphasizes how well these models work together to boost overall system security against advanced cyber threats and greatly increase detection accuracy.

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