Detection Of Malicious Urls Using Machine Learning Techniques

Document Type : Primary Research paper

Authors

1 Assistant Professor Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology

2 Final Year Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology

3 Final Year Student Department of Computer Science and Engineering Manakula Vinayagar Institute of Technology

Abstract

The net has become an essential portion of our everyday life for facts connection and knowledge diffusion. It helps in order to transact information regular, rapidly and quickly. Identifying theft in addition to identity fraud usually are referred as two sides of cyber-crime through which hackers in addition to malicious users get the personal information of current legitimate users to attempt fraud or deception determination for profit. Harmful URLs host unsolicited content (spam, phishing, drive-by exploits, and so forth. ) and attract unsuspecting users in order to become victims regarding scams (monetary reduction, theft of private information, and malware installation), and cause losses of billions of dollars each year. To find such crimes techniques should be quickly and precise along with the ability in order to discover new malicious content. Traditionally, this specific detection is carried out mostly with the usage of blacklists. However, blacklists should not be inclusive, and lack typically the ability to discover newly produced malicious URLs. To enhance the generality of malicious URL detectors, machine learning techniques have been explored along with increasing attention inside recent years. Inside this paper, I actually use a basic algorithm to discover and predicting Web addresses it truly is good or perhaps bad and in contrast to two other methods to know (SVM, LR).

Keywords

Int. J. of Aquatic Science
Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1980-1989

Files

Share

How to cite

Statistics