Machine Learning-Based Phishing Website Detection A Comprehensive Approach for Cyber security

In our contemporary lives where the internet plays a crucial role, the cybersecurity challenge posed by phishing attacks is noteworthy. This paper introduces an innovative strategy to tackle this issue by employing machine learning techniques for the identification of phishing websites. Harnessing advanced algorithms and feature extraction, our proposed model aims to differentiate between legitimate and malicious websites by analysing inherent patterns and characteristics of phishing attempts. Phishing remains a significant threat, causing substantial financial losses for internet users annually. This deceptive practice involves identity thieves using cunning strategies to trick individuals into disclosing confidential information. Phishers commonly employ tactics like forged emails and sophisticated phishing software to illegitimately obtain sensitive details, including usernames and passwords from financial accounts. This paper focuses on developing a robust system for identifying phishing websites, utilizing machine learning techniques. Specifically rooted in supervised learning, our methodology selects the Random Forest algorithm due to its exceptional classification performance. The primary objective of our study is to achieve the highest classification accuracy by thoroughly examining distinguishing features of both benign and phishing URLs. Through meticulous analysis, our goal is to identify the most effective combination of these features to train our classifier. As evidence of our efforts, we have achieved an impressive accuracy of 97% using the Random Forest algorithm, with the Logistic Regression method also demonstrating a commendable accuracy rate of 92%. We strongly recommend the adoption of our system as a sturdy defense against phishing attacks, ensuring the safety and security of internet users.