Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper

-

November Edition 2021

International Journal of Applied Information Systems solicits high quality original research papers for the November 2021 Edition of the journal. The last date of research paper submission is October 15, 2021.

Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm

Yogesh Dubey, Pranil Chaudhari, Shaldon Chaphya, Tina D’abreo. Published in Security

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Yogesh Dubey, Pranil Chaudhari, Shaldon Chaphya, Tina D’abreo
10.5120/ijais2017451660
Download full text
  1. Yogesh Dubey, Pranil Chaudhari, Shaldon Chaphya and Tina Dabreo. Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm. International Journal of Applied Information Systems 11(11):53-55, March 2017. URL, DOI BibTeX

    @article{10.5120/ijais2017451660,
    	author = "Yogesh Dubey and Pranil Chaudhari and Shaldon Chaphya and Tina Dabreo",
    	title = "Efficient Detection of Legitimate and Malicious URLs using ID3 Algorithm",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "March 2017",
    	volume = 11,
    	number = 11,
    	month = "Mar",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "53-55",
    	url = "http://www.ijais.org/archives/volume11/number11/973-2017451660",
    	doi = "10.5120/ijais2017451660",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, 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.

Reference

  1. 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
  2. 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.
  3. Sana Ansari and Jayant Gadge. “Architecture for Checking Trustworthiness of Websites “International journal of computer application, Volume 44, April 2012
  4. 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.
  5. 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.
  6. 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.
  7. 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.
  8. https://en.wikipedia.org/wiki/Uniform_Resource_Locator
  9. https://www.phishtank.com/developer_info.php
  10. https://en.wikipedia.org/wiki/ID3_algorithm

Keywords

Malicious URLs, Classifier, Feature Extraction, ID3 Algorithm