Augmented Framework for Enhanced Phishing Detection and Prevention

Abstract

Network security has become a hot topic as the Internet has grown in popularity. Phishing attacks are a type of cybercrime when a hacker poses as a reliable source to gain confidential data from a user of the internet. Therefore, more efficient phishing detection is required for better cyber defense. A framework for improving phishing detection based on machine learning is presented in this study. The framework attempts to enhance the speed-to-accuracy ratio in identifying fraud risks by examining the body of currently available literature on phishing attacks and associated remedies. The proposed model improves the effectiveness of phishing detection systems by highlighting the importance of data collection and filtering procedures. To create new security measures and respond to evolving threats, regular updates and ongoing research are necessary. Therefore, this study provides information for upcoming studies on cybersecurity and the preservation of personal data.