How can hybrid machine learning approaches (combining supervised and unsupervised learning) improve DDoS detection accuracy and robustness?

Hybrid approaches leverage the strengths of both methods. For example, unsupervised learning can pre-process data to identify potential anomalies, which are then fed into a supervised learning model for classification. This reduces the burden on the supervised model and improves its ability to detect novel attacks while maintaining accuracy on known attack types.