Phishing Attacks: Detection and Prevention Techniques
TL;DR
The research shows that while machine learning algorithms offer high detection accuracy, they require significant computational resources and continuous updates to remain effective against evolving phishing techniques and heuristic-based approaches, on the other hand, provide quick detection with lower resource demands but may struggle with new or sophisticated attacks.
Abstract
Phishing attacks are among the most prevalent and damaging cyber threats faced by individuals and organizations today. These attacks deceive users into revealing sensitive information, leading to significant financial and reputational damage. This paper explores various techniques for detecting and preventing phishing attacks, examining their effectiveness and implementation challenges. Through comprehensive experiments and analysis, we demonstrate the efficacy of different detection methods and propose best practices for mitigating phishing threats. Our study includes a detailed evaluation of machine learning algorithms, heuristic-based approaches, and user education programs, supported by experimental data and real-world case studies.Our research shows that while machine learning algorithms offer high detection accuracy, they require significant computational resources and continuous updates to remain effective against evolving phishing techniques. Heuristic-based approaches, on the other hand, provide quick detection with lower resource demands but may struggle with new or sophisticated attacks. User education programs are essential for long-term phishing prevention, as they empower users to recognize and avoid phishing attempts, significantly reducing the risk of successful attacks. By combining these methods, organizations can develop a robust defense strategy against phishing threats.