LI-HML detector: a unified framework for network traffic attack classification

Go to the profile of Hasna CHAMLAL
Hasna CHAMLAL and Hajar KAMEL
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LI-HML detector: a unified framework for network traffic attack classification - International Journal of Machine Learning and Cybernetics

International Journal of Machine Learning and Cybernetics - As cyber threats evolve in complexity and scale, accurately classifying network traffic is paramount for effective cybersecurity.…

presents the Local Interpretable hybrid meta-learner (LI-HML) to address critical challenges in attack detection and network traffic analysis. Key innovations include feature clustering for enhanced diversity, robust handling of imbalanced datasets through bootstrap sampling, and pre- and post-majority voting explanations. The framework not only achieves high performance but also delivers actionable insights into feature impor­tance and decision-making processes. This interpretable and effective approach positions LI-HML as a powerful tool for advancing cybersecurity and network traffic analysis.

Go to the profile of Hasna CHAMLAL

Contributors

Author

Go to the profile of Hasna CHAMLAL

Hasna CHAMLAL

PROFESSOR, COMPUTER SCIENCE AND SYSTEMS LABORATORY FACULTY OF SCIENCES AIN CHOCK HASSAN II UNIVERSITY OF CASABLANCA

Contributing author

Go to the profile of Hajar KAMEL

Hajar KAMEL

PhD student , Hassan II university

Multiple Contributors
Hasna CHAMLAL and Hajar KAMEL

Contributors

Author

Go to the profile of Hasna CHAMLAL

Hasna CHAMLAL

PROFESSOR, COMPUTER SCIENCE AND SYSTEMS LABORATORY FACULTY OF SCIENCES AIN CHOCK HASSAN II UNIVERSITY OF CASABLANCA

Contributing author

Go to the profile of Hajar KAMEL

Hajar KAMEL

PhD student , Hassan II university

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