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August 25, 2025MDPIOpen Access

Unveiling Cyber Threats: An In-Depth Study on Data Mining Techniques for Exploit Attack Detection

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An analysis finds efficient machine learning models for detecting cyber threats in large datasets, indicating critical cybersecurity improvements.

Efficiency of the Decision Tree model using Random Forest was shown in real-time exploit attack detection, highlighting its effectiveness.
Results demonstrated an accuracy of 87.9% with very short training time of 0.96 seconds on the UNSW-NB15 dataset.
The study applied five feature selection techniques and eight classification models to enhance exploit attack detection.
The research utilizes advanced data mining techniques to identify robust models for detecting cyber threats in increasing internet use.

Hyassat et al. (2025) studied this question.

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An analysis finds efficient machine learning models for detecting cyber threats in large datasets, indicating critical cybersecurity improvements.