Advanced Botnet Detection Using Hybrid Machine Learning Models

Title

Advanced Botnet Detection Using Hybrid Machine Learning Models

Creator

Chatterjee, Cheeradeep; George, Shiju; Jayapandian, N.

Description

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. 2025 IEEE.

Source

Proceedings of 2025 IEEE International Conference on Contemporary Computing and Communications, InC4 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Computer Network; Cyber Attack; Distributed Denial-of-Service; Internet of Things; Machine Learning; Random Forest

Coverage

Chatterjee C., Christ University, Dept. of Computer Science & Engg, Bangalore, India; George S., Christ University, Dept. of Computer Science & Engg, Bangalore, India; Jayapandian N., Christ University, Dept. of Computer Science & Engg, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833152118-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/InC465408.2025.11256249

https://www.scopus.com/pages/publications/105030320517?origin=resultslist