Abstract

Phishing website detection, utilizing machine learning algorithms, stands as a crucial defense mechanism against online scams and fraudulent activities, aiming to protect users from malicious endeavors. This abstract offers an overview of the applied methodology in detecting phishing websites through machine learning techniques. Phishing, characterized as a deceptive practice, involves attackers creating fraudulent websites that closely mimic legitimate ones, deceiving users into divulging sensitive information such as login credentials, financial data, or personal details.In response to this pervasive threat, researchers and cybersecurity experts have turned to machine learning as a potent solution. Various machine learning algorithms, including Random Forest, Support Vector Machine (SVM), Logistic Regression, etc., are applied to a feature-rich dataset. This dataset encompasses attributes like URL structure, content analysis, SSL certificate details, and more. The machine learning algorithm learns from labeled data, paving the way for the development of a predictive model proficient in distinguishing between phishing and legitimate websites.Phishing attacks have escalated into a significant cybersecurity threat, posing substantial risks to individuals and organizations on a global scale. This research introduces an innovative approach to detecting phishing websites, leveraging the capabilities of machine learning algorithms. The primary goal is to establish a robust and adaptive system, exhibiting high accuracy in identifying fraudulent websites. The envisioned outcome is the creation of a defense mechanism that effectively safeguards users from falling victim to deceptive online practices.