S. Umamaheswari,
S. Saranya,
- Professor Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University Tamil Nadu India
- Professor Department of Information Technology, C. Abdul Hakeem College of Engineering and Technology/Anna University Tamil Nadu India
Abstract
The goal of the abstract is to offer an automated method that uses login URLs to identify real-world scenarios. Phishing is a type of cyberattack that involves social engineering, when malefactors trick victims into providing their login credentials via a login form that sends the information to a hostile site. In this research, we offer a system that uses URL analysis to detect phishing websites by comparing machine learning and deep learning methodologies. The legitimate class in the majority of modern state-of-the-art phishing detection technologies consists of homepages without login fields. Instead, we utilize the login page URL in both classes, since we believe it to be a lot more realistic case scenario, and we show that testing with legitimate login page URLs yields a significant false-positive rate for existing methodologies. Additionally, we train a base model with old datasets and test it with new URLs, using datasets from different years to illustrate how models lose accuracy with time. Additionally, we run a frequency analysis across active phishing domains to find various tactics used by phishers in their campaigns. In order to validate these claims, we have developed a new dataset called Phishing Index Login URL (PILU-90K), which consists of 30K phishing URLs and 60K authentic URLs, such as index and login webpages.
Keywords: Phishing index, Frequency, traffic, PyCharm, python libraries, naïve bayes
[This article belongs to Journal of Telecommunication, Switching Systems and Networks(jotssn)]
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Journal of Telecommunication, Switching Systems and Networks
| Volume | 11 |
| Issue | 02 |
| Received | July 6, 2024 |
| Accepted | July 10, 2024 |
| Published | July 23, 2024 |