Research Article
Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm
|
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
|
| Volume 11 - Issue 11 |
| Published: Mar 2017 |
| Authors: Yogesh Dubey, Pranil Chaudhari, Shaldon Chaphya, Tina D�Abreo |
10.5120/ijais2017451660
|
Yogesh Dubey, Pranil Chaudhari, Shaldon Chaphya, Tina D�Abreo . Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm. International Journal of Applied Information Systems. 11, 11 (Mar 2017), 53-55. DOI=10.5120/ijais2017451660
@article{ 10.5120/ijais2017451660,
author = { Yogesh Dubey,Pranil Chaudhari,Shaldon Chaphya,Tina D�Abreo },
title = { Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm },
journal = { International Journal of Applied Information Systems },
year = { 2017 },
volume = { 11 },
number = { 11 },
pages = { 53-55 },
doi = { 10.5120/ijais2017451660 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2017
%A Yogesh Dubey
%A Pranil Chaudhari
%A Shaldon Chaphya
%A Tina D�Abreo
%T Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm%T
%J International Journal of Applied Information Systems
%V 11
%N 11
%P 53-55
%R 10.5120/ijais2017451660
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Malicious websites are one of the serious threat over the internet. Ever since the inception of the internet, there has been a rise in malicious content over the web such has terrorism, financial fraud, phishing and hacking that targets user’s personal information. Till date, the various systems have been used for the detection of a malicious website based on text and content of the websites. This method has some disadvantages and the numbers of victims have therefore continued to increase. Here we developed a system which helps the user to identify whether the website is malicious or not. Our system identifies whether the site is malicious or not through URL. The proposed system is fast and more accurate compared to current system. The classifier is trained with datasets of 1000 malicious sites and 1000 legitimate site URLs. Trained classifier is used for detection of malicious URLs.
References
- Ma, Justin, et al. "Beyond Blacklists: learning to detect malicious websites from suspicious URLs." Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2009
- Jin-Lee Lee,Doung-Hyun Kim,Chang-Hoon Lee. “Heuristic-based Approach for Phishing Site Detection Using URL Features” Third Intl. Conf. on Advances in Computing, Electronics and Electrical Technology - CEET 2015.
- Sana Ansari and Jayant Gadge. “Architecture for Checking Trustworthiness of Websites “International journal of computer application, Volume 44, April 2012
- Mustafa Aydin and Nazife Baykal “Feature Extraction and Classification Phishing Websites Based on URL” Cyber Defence and Security Laboratory of METU-COMODO, IEEE CNS 2015.
- Nguyen, Luong Anh Tuan, et al. "A novel approach for phishing detection using URL-based heuristic." Computing, Management and Telecommunications (ComManTel), 2014 International Conference on. IEEE, 2014.
- Canali, Davide, et al. "Prophiler: a fast filter for the large-scale detection of malicious web pages." Proceedings of the 20th international conference on World wide web. ACM, 2011.
- Sumalatha Ramachandran, Sujaya Paulraj, Sharon Joseph and Vetriselvi Ramaraj, “Enhanced Trustworthy and High-Quality Information Retrieval System for Web Search Engines”, IJCSI International Journal of Computer Science Issues, Vol. 5, October 2009, pp38-42.
- https://en.wikipedia.org/wiki/Uniform_Resource_Locator
- https://www.phishtank.com/developer_info.php
- https://en.wikipedia.org/wiki/ID3_algorithm
Index Terms
Keywords