A framework for handling class imbalance in malicious URL dataset

Key Points

• Improved classification accuracy for malicious URLs leads to better cybersecurity.
• The framework includes novel algorithms for addressing class imbalance.
• Analysis involves techniques from the fields of machine learning and anomaly detection.
• The findings indicate the need for robust strategies in cybersecurity datasets.