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Reseach Article

Model for Detection of Phishing Sites using Clustering and Bayesian Approach: A Survey

Published on May 2016 by Nilima Ramdas Narad, Sandeep U. Kadam
National Conference on Advancements in Computer & Information Technology
Foundation of Computer Science USA
NCACIT2016 - Number 1
May 2016
Authors: Nilima Ramdas Narad, Sandeep U. Kadam
d7848839-24ff-477d-8826-da20ce7e9a47

Nilima Ramdas Narad, Sandeep U. Kadam . Model for Detection of Phishing Sites using Clustering and Bayesian Approach: A Survey. National Conference on Advancements in Computer & Information Technology. NCACIT2016, 1 (May 2016), 17-19.

@article{
author = { Nilima Ramdas Narad, Sandeep U. Kadam },
title = { Model for Detection of Phishing Sites using Clustering and Bayesian Approach: A Survey },
journal = { National Conference on Advancements in Computer & Information Technology },
issue_date = { May 2016 },
volume = { NCACIT2016 },
number = { 1 },
month = { May },
year = { 2016 },
issn = 0975-8887,
pages = { 17-19 },
numpages = 3,
url = { /proceedings/ncacit2016/number1/24698-3032/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Conference on Advancements in Computer & Information Technology
%A Nilima Ramdas Narad
%A Sandeep U. Kadam
%T Model for Detection of Phishing Sites using Clustering and Bayesian Approach: A Survey
%J National Conference on Advancements in Computer & Information Technology
%@ 0975-8887
%V NCACIT2016
%N 1
%P 17-19
%D 2016
%I International Journal of Computer Applications
Abstract

Web Phishing is a major attack nowadays. web phishing is phisher tries to get users sensitive information like bank details, ATM pin or any personal information. After extracting users information attacker may misuse this information. Nowadays most of the people prefer online shopping or online payment and user has to share his personal or sensitive information on web page. User may not think about website security. So website security is very important. Before doing any transaction and sharing any personal information on web page, user must ensure the security of that website. The best solution for this problem is to protect from phishing is to identify a phish. Phishing emails usually seem to come from well-known organization and ask your personal information such as credit card number, security number, account number or passwords. What actually attacker does? The attacker creates the no of replicas of authenticate sites, and users are forced to direct to that websites by attracting them with offers. As standard mentioned in W3C (World Wide Web Consortium), I am proposing a system which can easily recognize the difference between authenticate site and phishing site. There are certain standards which are given by W3C (World Wide Web Consortium), based on these standards I am choosing some features which can easily describe the difference between legit site and phish site. To protect you from phishing, I am proposing a model to determine the fraud sites. To determine the phishing attack, URL features and HTML features of web page are considered. Clustering algorithm such as K-Means clustering is applied on the database and prediction techniques such as Naive Bayes Classifier is applied. By applying this, probability of the web site as valid Phish or Invalid Phish. To check the validity of URL, if still user is not able decide the validity of web page then Naïve Bayes Classifier is applied.

References
  1. RachnaDhamija, J. D. Tygart, and Marti Heast, "Why Phishing Works", CHI-2006, Conference on Human Factor in Computing Systems, April 2006.
  2. Phishing Activity Trend report 2nd quarter 2014,http://www. apwg. org.
  3. RSA Online Fraud Surveyor, "The phishing kit – the same wolf, just different sheep's clothing", RSA Surveys, vol-1, February-2013.
  4. Xiaoping GU, Hong Yuan WANG, and Tonguing NI "An Efficient Approach to Detect Phishing Web" Journal of Computational Information Systems 9:14(2013), 2013, pp. 5553-5560.
  5. Computational Information Systems 9:14(2013), 2013, pp. 5553-5560.
  6. Haijun Zhang, Gang Liu, Tommy W. S. Chow, Senior Member, IEEE, and Wenyin Liu, Senior Member, IEEE "Textual and Visual Content-Based Anti-Phishing: A Bayesian Approach", vol-22, IEEE Transactions October- 2011 pp. 1532-1546.
  7. Angelo P. E. Rosiello, EnginKirda, Christopher Kruegel, FabrizioFerrandi, and Politecnico di Milano "A Layout-Similarity-Based Approach for Detecting Phishing Pages"- unpublished
  8. WIKIPEDIA. ORG- The Online Encyclopedia, http://www. wikipedia. org/
  9. Abraham Sillberschatz, Henry Korth, and S. Sudarshan, "Database System Concepts", 5th Edition, pp. 900-903.
  10. PHISHTANK. COM- The Online Valid Phish Sites Repository,http://data. phishtank. com/data/online valid. csv
Index Terms

Computer Science
Information Sciences

Keywords

Anti Phishing Technique Bayesian Approach Data Mining Database Clustering And Phishing Attacks.