Phisheild Website Detection System for Safeguarding Online Security

Abstract

The increasing prevalence of cyber threats increases the need to identify effective phishing websites. Phishing attacks, often disguised inside legitimate-looking websites, compromise necessary details that needs to be kept private can cause big problems for people and groups, giving urgent importance as a potential economic loss , identity theft, and data breaches associated with successful phishing attempts are high. Machine learning algorithms have an important role to play in this context, with Light GBM emerging as a particularly useful approach. Its intrinsic capacity to effectively handle huge quantities data and arrive at swift and accurate conclusions distinguishes it from other models Light GBM is effective in handling imbalanced data, and its powerful object analytics capabilities make it a powerful tool for understanding subtle patterns of indicators phishing activities enlightens Instead, it sets a benchmark for efficiency and reliability in cyber-security In in this research, we solve the important challenge of phishing website detection by exploiting the features of URL domains. A detailed analysis of the datasets resulted in a robust model, in which Light GBM was identified as the final model of choice The metrics for each class include precision, recall, and F1-score,benign, defacement, phishing and malware were meticulously assessed, demonstrating The algorithm's capabilities to effectively identify phishing websites. The research results do additionally assist to the advancements in hacking systems for detection, underscore the significance of employing Light GBM for enhanced classification accuracy. This research establishes a foundation for future endeavors in web security and holds practical implications for improving cybersecurity measures to protect users from potential threats.