MALICIOUS URL DETECTION USING MACHINE LEARNING AND DEEP LEARNING
Author(s):
PRASANNA KUMAR M, Dhanraj, Bhavanishankar K
Keywords:
phishing, machine learning, malicious URL detection
Abstract
The quantity and magnitude of network information security risks have continually rising. Hackers today mostly employ techniques that target technology from beginning to conclusion and take advantage of human weakness. These methods include pharming, phishing, and social engineering, among others. These attacks include a number of phases, one of which is to trick users through malicious Uniform Resource Locators (URLs). In light of this, malicious URL detecting is a hot subject right now. A variety of academic research have demonstrated several ways to identify malicious URLs using machine learning and deep learning technologies. Based on our hypothesized URL behaviours and characteristics, we provide a machine learning-based solution for detecting malicious URLs in this work. Furthermore, big data technology is applied to enhance the ability to appreciate fraudulent URLs based on aberrant activity. A novel collection of URL traits and behaviours, a machine learning algorithm, and big data technologies make up the suggested detection method, to summarize it. The experimental findings indicate that the specified URL features and behaviour can increase total the capacity to identify dangerous URLs. This suggests that the proposed methodology may be seen as a successful and consumer method of identifying dangerous URLs.
Article Details
Unique Paper ID: 159906

Publication Volume & Issue: Volume 9, Issue 12

Page(s): 768 - 774
Article Preview & Download


Share This Article

Conference Alert

NCSST-2023

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2023

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2023

Go To Issue



Call For Paper

Volume 10 Issue 1

Last Date for paper submitting for March Issue is 25 June 2023

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews