Safeguarding Cyber Ecosystems: Machine Learning and Data Mining Approaches for Proactive Phishing Detection in Websites

Abstract

Abstract: The prevalence of cyber threats, notably phishing attacks, presents a significant challenge in the digital realm with the increasing reliance on the internet. Phishing, where victims' credentials are illicitly acquired through deceptive websites resembling legitimate ones, is a particularly concerning form of cybercrime. This paper proposes an innovative system utilizing Machine Learning and Data Mining techniques to detect both established and newly generated phishing URLs without historical behavioral data. The system, embodied in a Chrome browser plugin, operates in real-time to provide immediate protection as users browse web pages. A distinctive feature of this system is its capability to identify phishing websites lacking prior behavioral patterns, ensuring adaptability to evolving cyber threats. Through comprehensive training with an extensive dataset, the model powering the plugin aims for high accuracy in detecting phishing attempts. By deploying advanced Machine Learning methodologies, the system discerns subtle patterns and characteristics intrinsic to phishing URLs, effectively distinguishing them from legitimate websites. The ultimate objective is to enhance cyber security by providing users with a robust and proactive tool against the growing sophistication of phishing attacks, thereby contributing to the ongoing efforts to create a secure online environment amidst the dynamic landscape of cyber threats.