Scalable Malicious URL Classification: Leveraging Lexical Analysis and API Integration

In the digital age, the proliferation of malicious Uniform Resource Locators(URLs) pose a significant cyber security threat, enabling phishing attacks, malware infections, and data breaches that risk sensitive information and user safety. Traditional methods using static blacklists and heuristics are ineffective against evolving threats. This study proposes a machine learning-based approach to enhance malicious URL detection, utilizing nine lexical features for efficient phishing attack identification. Leveraging the ISCXURL-2016 dataset, comprising 11,964 legitimate and phishing URLs, our model demonstrates effective detection capabilities suitable for resource constrained devices. The classification model accurately distinguishes between benign and malicious URLs by examining URL-derived features, metadata, and Application Program Interface(API) integration. This approach addresses the limitations of existing methods, which rely on numerous features requiring substantial processing power. The objective is to develop a dynamic, scalable system capable of identifying new, unseen malicious URLs with high accuracy.