Fortfying The Digital Landscape Publishing URL Detection
1 Mr.P.Naveen Kumar (Assistant Professor),
Arr.Rohit 2, H.Shirisha 3, M.Chaithanya 4
2ARR.rohit Department of Computer Science and Engineering (Joginpally B.R Engineering College)
3H.Shirisha Department of Computer Science and Engineering (Joginpally B.R Engineering College)
4 M.Chaithanya Department of Computer Science and Engineering (Joginpally B.R Engineering College)
ABSTRACT
The increasing prevalence of cyber threats and malicious activities in the digital landscape has underscored the need for effective detection and prevention mechanisms, particularly in the context of malicious URLs. This project aims to strengthen digital security by detecting malicious URLs, a common tool for cyber threats such as phishing, malware, and data breaches. With the surge in cyberattacks, traditional blacklist-based and signature detection methods struggle to identify new, sophisticated malicious links in real time. To address this, our project leverages machine learning techniques to classify URLs as safe or harmful, offering a proactive approach to user protection. Our system analyze URLs by extracting key features, including length, special characters, domain age, and lexical patterns. It combines these with host-based attributes to detect malicious tendencies. We enhance detection performance through ensemble learning and continuous model optimization using real-world data. This approach enables our solution to identify potential threats instantly, helping to secure users from interacting with harmful URLs. By offering an effective layer of defense against evolving threats, our project contributes to cybersecurity advancements, reinforcing the digital landscape and improving online safety for individuals and organizations alike. This work contributes to the ongoing development of safer digital environments by offering actionable insights and practical solutions for enhancing URL security protocols
