LinkQuell: A Hybrid Phishing URL Detection and Analysis Platform

Authors:

Sheeba C¹, Bharathkumar M ² , Deepak N ³, Jayasurya J ⁴ , Kalaivendhan B⁵

¹Assistant Professor, ^2-5Student, Department of Information Technology , Meenakshi College of Engineering

Abstract

Phishing attacks remain a serious issue in today's internet environment, exploiting deceptive URLs and phishing websites. However, conventional phishing detection methodologies have become less effective as attackers develop more sophisticated techniques. This paper describes LinkQuell, a multi-method phishing detection and analysis system combining different detection methods like heuristic rules, machine learning algorithms, HTML analysis, domain reputation, and behavior-based analysis.

Apart from detecting the phishing links, the system explains the decision behind its choices. In this regard, it becomes easy for individuals to understand the process followed by the software. The system utilizes an algorithm that is continually improved through user feedback on various phishing attempts in order to enhance its capability in the detection of threats.

The software has been developed using FastAPI as the back end, React as the front end, browser extensions, and SQLite as the database.In tests done on the software, it has been noted that the ability of the system in the detection of threats is continuously improved with the passage of time since it continues to learn from the experiences of the users.

The software contains an interactive interface that allows collaboration among users, hence making it possible for people to interact and detect any possible threat on the Internet. The phishing link detection system allows users to improve the software hence making them capable of interacting online.

Moreover, the emergence of LinkQuell is a response to the increasing demand for a security system that not only provides strong security in terms of technology but can also be easily used by common users. Through the combination of several analytical layers in a single system, it ensures balance between fast detection and thoroughness. In addition, the inclusion of real-time scanning via a browser extension makes it more user-friendly.

Keywords

Phishing Detection, Cybersecurity, URL Analysis, Hybrid Detection System, Machine Learning, Heuristic Analysis, HTML Inspection, Domain Intelligence, Behavioral Analysis, Explainable AI, Feedback-Based Retraining, Real-Time Detection, Browser Extension, Threat Detection.