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– ABSTRACT
1. Introduction: In this digital age, where online interactions have become integral to our daily lives, phishing attacks have become more sophisticated and prevalent. This paper addresses the urgent need for a proactive approach to identify and prevent phishing attempts.
2. Methodology: Our approach employs a combination of machine learning algorithms, heuristic analysis, and real-time website monitoring. Through continuous learning, the system adapts to new phishing techniques, enhancing its accuracy and effectiveness.
3. Identification Stage: The system begins by analyzing website characteristics, examining URL structures, and scrutinizing content for common phishing indicators. Machine learning models, trained on vast datasets of known phishing websites, actively classify and identify potential threats.
4. Proactive Prevention: Upon identifying a potential phishing website, the system takes immediate preventive measures. Utilizing an active firewall, the system blocks access to the malicious site and alerts users in real-time, mitigating the risk of falling victim to phishing attacks.
5. Continuous Learning and Adaptation: The system’s strength lies in its ability to adapt to emerging phishing techniques. Through continuous learning, it refines its detection capabilities, staying ahead of cyber threats and ensuring a proactive defense strategy.
6. Conclusion: The proposed system presents a formidable solution to the ever-growing menace of phishing attacks. By combining advanced technologies, real-time monitoring, and proactive prevention measures, this approach significantly enhances the security posture of users in the digital realm.
In conclusion, the proactive identification and prevention of phishing websites are critical in safeguarding sensitive information and maintaining a secure online environment. This system provides an effective and adaptive solution to counter the evolving landscape of cyber threats.
