Paper Key : IRJ************060
Author: Sahil.nilesh.shah
Date Published: 10 Nov 2024
Abstract
With the swell in phishing and malware pitfalls, malicious link discovery is one of the increasingly important aspects of cybersecurity. The proposed model relies on advanced machine learning techniques to distinguish between legitimate and dangerous links, carrying out comprehensive analysis of various characteristics of linksURL patterns, lexical features, and embedded metadatafor high accuracy in real-time discovery. It applies a multi-layered classification approach along with supervised learning and heuristics for better detection. This tool has been tested extensively over an expansive dataset of URLs consisting of benign and malicious samples. The cases studied illustrate the effectiveness of the model. Thus, this paper contributes to advanced online security, providing an effective, reliable, and scalable solution for threat discovery from malicious links. Keywords: Malicious Link Discovery, Cybersecurity Research, Threat Analysis, Machine Learning Models, Phishing Detection, URL Pattern Analysis, Heuristic Design, Real-Time Threat Discovery.
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