Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/2579
Title: Malware Detection In Url Using Machine Learning Approach
Authors: Kumar, Rajesh
Talwar, Rachit
Sharma, Manik
Kumari, Suchi
Goel, Shivani
Malhotra, Kanika
Ahmed, Faiz
Keywords: Black Listing
Machine Learning
Malicious URLs
Malware Detection
Issue Date: 2023
Publisher: Advanced Computing: 12th International Conference, IACC 2022
Citation: Vol. 1782 CCIS. ; pp. 251-263
Abstract: The most common technique to host fraudulent or harmful content, such as spam, malicious ads, etc., is using a Uniform Resource Locator (URL). Malicious URLs hold harmful contents that cause loss of information, malware installation, and monetary loss of the victims. Hence, it is necessary to detect such URLs and take some action on such threats. Earlier, one database is maintained to blacklist such URLs and a URL is compared with the available database of blacklisted URLs. If the URL is found in the database then the browser considers the URL suspicious and blocks it. But, this method is ineffective in finding newly discovered URLs. By suggesting a solution based on machine learning, this problem can be resolved. This research aims to investigate how machine-learning techniques can be used to identify harmful URLs. © 2023, Springer Nature Switzerland AG.
URI: https://doi.org/10.1007/978-3-031-35644-5_20
http://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/2579
ISBN: 9783031356438
9783031356445
ISSN: 1865-0929
1865-0937
Appears in Collections:Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.