Using WCS-GNB for intrusion detection: improving Gaussian Naive Bayes with class-specific weights and validating against different machine learning models

The increasing number of cyber threats demands a robust, real-time detection system that can accurately classify attacks while maintaining computational efficiency in real-time and within reasonable resource limits. Most real-time applications in cybersecurity still rely on traditional machine learning methods with arbitrary configurations due to the difficulty in resolving the trade-off between accuracy and speed within the system. This work proposes a modification to the standard Gaussian Naiv