Enhancing Cybersecurity: Machine Learning Techniques for Phishing URL Detection
Phishing attacks exploit user vulnerabilities in cybersecurity awareness by tricking them to fake websites designed to steal confidential data. This study proposes a method for detecting phishing URLs using machine learning. The proposed method analyzes various URL characteristics, such as length, subdomain levels, and the presence of suspicious patterns, which are key indicators of phishing attempts. Gradient Boosting was selected due to its robustness in handling complex, non-linear relationships between features, making it particularly effective in distinguishing between legitimate and phishing URLs, by evaluating the Gradient Boosting classifier on a dataset with 10,000 entries and 50 features, the method achieves an accuracy of 99%.This approach has the potential to enhance web browsers with add-ons or middleware that alert users from potential phishing sites which will be based solely on URL. 2024 IEEE.
Garnayak S.; Kumar A.; Sahoo S.; Gouda B.; Sharma V.
2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024
Institute of Electrical and Electronics Engineers Inc.
https://doi.org/10.1109/ICEECT61758.2024.10739207 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85211189476&doi=10.1109%2fICEECT61758.2024.10739207&partnerID=40&md5=289f0aa1a7fade2a0e86ea8c76dcb18a
Garnayak S., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Kumar A., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Sahoo S., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Gouda B., C.V Raman Global University, Computer Science And Engineering, Bhubaneswar, India; Sharma V., CHRIST University, Computer Science Department, Delhi NCR, India