September 20, 2025

Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach

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The proposed framework achieves a remarkable accuracy of 99.7% in identifying network-based attacks.
Using ensemble machine learning methods, the model shows a recall of 98.4% and precision of 99.4%, highlighting performance.
Research incorporates advanced machine learning and optimization techniques for effective attack classification.
Feature extraction methods including statistical and behavioral features enhance model performance for attack detection.

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Implication

Research demonstrates improved accuracy and adaptability in detecting network-based attacks using machine learning, indicating enhanced cybersecurity capabilities.

Cite This Study

Kalantri et al. (2025) studied this question.

synapsesocial.com/papers/68d43c87713b0b5dfea7c359 https://doi.org/10.21203/rs.3.rs-6472892/v1

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Implication

Research demonstrates improved accuracy and adaptability in detecting network-based attacks using machine learning, indicating enhanced cybersecurity capabilities.

Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach

Key Points

Discussion

Implication

Cite This Study

Robust Framework for Detecting and Classifying Network-Based Attacks Using Ensemble Machine Learning Approach

Key Points

Discussion

Implication

Cite This Study

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