Conventional blacklist-based detection methods

The gap

Evidence profile

Sourced from the stated research gap and future work and conclusions of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 4 representative gaps

• AI-Powered Detection of Fraudulent Web Platforms Using Behavioral and Structural Analysis (2026) · International Research Journal on Advanced Engineering Hub (IRJAEH) · doi

  Traditional rule-based detection systems are no longer effective against dynamically evolving phishing techniques. The rapid digitalization of banking, e-commerce, social networking, and government services has significantly increased reliance on web platforms while simultaneously expanding cybersecurity threats.

  generalstated research gap
  Keywords: traditional rule-based detection systems longer effective against dynamically
• PhishAlert: A Risk-Based URL Detection System for Identifying Phishing Websites (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  • Incorporate machine learning for detecting phishing attacks • Create an application for use on mobile phones • Include real-time URL analysis capability • Upgrade detection algorithms to counter new methods of phishing attacks • Increase accuracy and performance REFERENCES J. Ma, L. K. Saul, S. Savage, and G. M. Voelker, “Beyond blacklists: Learning to detect malicious web sites from suspicious URLs,” Proceedings of the ACM SIGKDD, pp. 1245–1254, 2009. A. Le, A. Markopoulou, and M. Faloutsos, “PhishDef: URL names say it all,” IEEE INFOCOM, pp. 191–195, 2011. M. Khonji, Y. Iraqi, and A. Jones, “Phishing detection: A literature survey,” IEEE Communications Surveys & Tutorials, vol. 15, no. 4, pp. 2091–2121, 2013. R. Verma and N. Hossain, “Semantic feature selection for text with application to phishing email detection,” IEEE Conference on Communications and Network Security, pp. 455–463, 2014. S. Garera, N. Provos, M. Chew, and A. D. Rubin, “A framework for detection and measurement of phishing attacks,” ACM Workshop on Recurring Malcode, pp. 1–8, 2007. N. Abdelhamid, A. Ayesh, and F. Thabtah, “Phishing detection based on associative classification data mining,” Expert Systems with Applications, vol. 41, no. 13, pp. 5948–5959, 2014. S. Rao and K. Kumar, “Phishing website detection using URL-based feature extraction and classification,” International Journal of Advanced Computer Science and Applications, vol. 11, no. 5, pp. 210–216, 2020. P. Prakash, M. Kumar, R. R. Kompella, and M. Gupta, “PhishNet: Predictive blacklisting to detect phishing attacks,” IEEE INFOCOM, pp. 1–5, 2010. A. K. Jain and B. B. Gupta, “Phishing detection: Analysis of visual similarity based approaches,” Security and Communication Networks, vol. 2017, pp. 1–20, 2017. S. Marchal, J. Francois, R. State, and T. Engel, “PhishStorm: Detecting phishing with streaming analytics,” IEEE Transactions on Network and Service Management, vol. 11, no. 4, pp. 458–471, 2014. VII. CONCLUSION In terms of the project PhishAlert – A Risk-Based URL Detection System, we have a good approach that will help you find phishing links effectively through rule-based analysis and verification via the external website.

  generalfuture workevidence 5/5
  Keywords: phishing detection ieee based attacks learning detecting application real time detect infocom communications feature network
• Deep Learning-Based Phishing URL Detection Using Deep Neural Network and Convolutional Neural Networks (2026) · Journal of Cyber Law · doi

  The proposed deep learning-based approach contributes to the development of automated cybersecurity systems that can assist in identifying malicious websites and reducing the risks associated with phishing attacks, while future research may focus on incorporating larger datasets, exploring hybrid deep learning architectures, and developing real time phishing detection systems for practical cybersecurity applications.

  generalconclusionsevidence 5/5
  Keywords: deep learning cybersecurity systems phishing proposed based approach contributes development automated assist identifying malicious websites
• Comparison of the Performance of Machine Learning Classification Algorithms on Phishing URL Detection (2026) · Jurnal Inotera · doi

  Conventional blacklist-based detection methods are considered less effective in recognizing new phishing URLs that continue to develop dynamically. There is a need to analyze and compare the performance of various machine learning and deep learning algorithms in accurately detecting phishing URLs.

  generalstated research gapevidence 5/5
  Keywords: conventional blacklist-based detection methods considered less effective recognizing

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