

Website Phishing Detector
Abstract
Phishing attempts are still one of the most popular techniques to deceive customers and steal personal information, especially with the significant increase in cyberthreats. This project provides a browser extension-based phishing detection solution to increase user security when using the internet. The plugin continuously examines and assesses website URLs, data, and content to identify any phishing attempts. It makes use of databases of blocked URLs, machine learning techniques, and heuristic criteria to accurately identify questionable websites. When a threat is detected, users receive an immediate alert, allowing them to avoid risky websites. This lightweight, user-friendly plugin aims to provide a seamless and effective layer of security without compromising surfing speed, making it an essential tool for safer online experiences.
References
Sahoo, Doyen, Chenghao Liu, and Steven C.H. Hoi. "Malicious URL Detection using Machine Learning: A Survey." ACM Computing Surveys, Vol. 53, No. 4, ACM, 2021.
Marchal, Samuel, et al. "Know Your Phish: Novel Techniques for Detecting Phishing Sites and Their Targets." IEEE Transactions on Dependable and Secure Computing, IEEE, 2020.
Verma, Rakesh, and Amitrajit Das. "What's in a URL: Fast Feature Extraction and Machine Learning-based URL Phishing Detection." 2017 IEEE International Conference on Semantic Computing (ICSC), IEEE, 2017.
Xiang, Guanhua, et al. "Cantina+: A Feature-rich Machine Learning Framework for Detecting Phishing Web Sites." ACM Transactions on Information and System Security (TISSEC), Vol. 14, No. 2, ACM, 2011.
Bahnsen, Alejandro Correa, et al. "Machine Learning for Phishing Detection: An Evaluation of Support Vector Machines, Decision Trees, and Neural Networks." 2017 IEEE
International Conference on Big Data (Big Data), IEEE, 2017.
Alsharnouby, Mahmoud, Fadi Alaca, and Sonia Chiasson. "Why Phishing Still Works: User Strategies for Combating Phishing Attacks." International Journal of Human- Computer Studies, Vol. 82, Elsevier, 2015.
Mohammad, Rami M., Fadi Thabtah, and Lee McCluskey. "An Anti-phishing Approach that Uses Phishing Characteristics in Uniform Resource Locator." Information Security Journal, Springer, 2015.
Basnet, Rajesh, Andrew H. Sung, and Qiang Liu. "Learning to Detect Phishing URLs." International Journal of Research in Computer Science, Vol. 2, No. 1, 2012.
Rathod, Dipika V., Dhruv Patel, and Harsh Bhavsar. "A Review: Browser Extension to Detect Phishing Attack Using Machine Learning." International Journal of Computer Applications (IJCA), Vol. 183, No. 2, 2021.
Abdelhamid, Noura, Ayman Ayesh, and Fadi Thabtah. "Phishing Detection: A Recent Intelligent Machine Learning Comparison Based on Models Content and Features." IEEE Access, IEEE, 2014.
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