Advanced Threat Detection: Leveraging Data Analytics for Cybersecurity Enhancement

Phishing is a continuous problem in the field of cybersecurity that puts people and businesses at risk. It targets confidential material like financial records and passwords. Traditional methods of detecting phishing attempts often struggle to keep pace with the evolving strategies of attackers. In respond, this study introduces a novel approach leveraging machine learning methods for identifying malicious URLs. Through analyzing dataset of labeled URLs, we employ feature engineering to extract pertinent characteristics indicative of phishing behavior. Next, in order to distinguish genuine URLs from phishing ones, we train and evaluate a variety of machine learning techniques, such as Light GBM, random forests, and XGBoost. Our experimental findings showcase promising results, with high accuracy rates achieved while effectively minimizing false positives. This proposed approach stands as a proactive defense mechanism against phishing attacks and underscores the potential of machine learning in fortifying cybersecurity defenses.