Phishing Websites Detection using Machine Learning

In the era of digital world, phishing attempts have emerged as one of the most pervasive and deceptive cyber risks. Phishing attacks pose a significant threat to internet users, targeting sensitive information through misleading websites that mimic legitimate ones. Cybercriminals utilize deceitful strategies to trick unwary users into disclosing private information, which can have disastrous results including identity theft, financial losses, and compromised data security. Our proposed system uses machine learning techniques that have become increasingly popular as a reliable method for spotting phishing websites as a means of addressing this rising issue with minimal system requirements where ML algorithms need limited resources than Deep learning algorithms. To improve phishing website identification, the accuracy and efficiency of various machine learning algorithms are assessed and compared using tabulation. The trained model is used in conjunction with a website to classify URLs as legitimate versus phishing attempts. Leveraging machine learning techniques, phishing detection systems have significantly improved in recent years, providing refined and precise results. As a result of these advancements, both internet users and organizations are better protected against phishing attacks. Users can navigate the web with increased trust, knowing that potential malicious websites will be identified and blocked effectively. The proposed system predicts URLs with increased accuracy of 13% among all the existing systems that uses machine learning algorithms for URL state prediction.