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Phishing Website Detection Using Particle Swarm Optimization
Radha Damodaram , M.L.Valarmathi
Pages - 477 - 490     |    Revised - 01-11-2011     |    Published - 15-12-2011
Volume - 5   Issue - 5    |    Publication Date - November / December 2011  Table of Contents
Fake Website, Association and Classification Technique, ACO
Fake websites is the process of attracting people to visit fraudulent websites and making them to enter confidential data like credit-card numbers, usernames and passwords. We present a novel approach to overcome the difficulty and complexity in detecting and predicting fake website. There is an efficient model which is based on using Association and classification Data Mining algorithms combining with ACO algorithm. These algorithms were used to characterize and identify all the factors and rules in order to classify the phishing website and the relationship that correlate them with each other. It also used PART classification algorithm to extract the phishing training data sets criteria to classify their legitimacy. But, this work has limitations like Sequences of random decisions (not independent) and Time to convergence uncertain in the phishing classification. So to overcome this limitation we enhance Particle Swarm Optimization (PSO) which finds a solution to an optimization problem in a search space, or model and predict social behavior in the presence of phishing websites. This will improve the correctly classified phishing websites. The experimental results demonstrated the feasibility of using PSO technique in real applications and its better performance. This project employs the JAVA technology.
CITED BY (7)  
1 Anchor, Nafisa, & Abdul race. (2015). Identify phishing websites in internet banking using optimization algorithm inclined pages. Electronic and cyber defense Journal, 3 (1).
2 Anchor , & Abdul race. ( 2015). Identify phishing in online banking site using the optimization algorithm inclined plates . Journal of electronic and cyber defense , 3 ( ??1 ).
3 Emilin, S. C. (2014). Detecting And Preventing Phishing Websites Dppws.
4 Behdad, M. (2014). Application of Learning Classifier Systems to Fraud Detection Problem (Doctoral dissertation, University of Western Australia).
5 Varughese, N. M. (2014). collaborative network security management system based on association mining rule. ictact journal on soft computing, 4(4).
6 Krishnakumar, L., & Varughese, N. M. (2013, December). High speed classification of vulnerabilities in cloud computing using collaborative network security management. In Advanced Computing and Communication Systems (ICACCS), 2013 International Conference on (pp. 1-6). IEEE.
7 Behdad, M., Barone, L., Bennamoun, M., & French, T. (2012). Nature-inspired techniques in the context of fraud detection. Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, 42(6), 1273-1290.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 Bielefeld Academic Search Engine (BASE) 
6 Scribd 
7 SlideShare 
8 PdfSR 
1 A. Hossain, M. Dorigo, Ant colony optimization web page, http:// iridia.ulb.ac.be / mdorigo/ACO/ACO.html N. Ascheuer, Hamiltonian path problems
2 Ant Colony Optimization, Vittorio Maniezzo, Luca Maria Gambardella, Fabio de Luigi.
3 Optimizing Large Scale Combinational Problems Using Multiple Ant Colonies Algorithm based on Pheromone Evaluation technique, Alaa Aljanaby, Ku Ruhana Ku Mahamud,
4 Associative Classification Techniques for predicting e-Banking Phishing Websites, Maher Aburrous Dept. of Computing ,Universit y of BradfordBradford, UK.
5 B. Adida, S. Hohenberger and R. Rivest , "Lightweight Encryption for Email," USENIX Steps to Reducing Unwanted Traffic on the Internet (SRUTI), 2005 ,
6 Bing Liu, Wynne Hsu, Yiming Ma, ”Integrating Classification and Association Rule Mining." Proceedings ofthe Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation), New York, USA.
7 GARTNE R, INC. Gartner Says Number of Phishing Emails Sent to U.S. Adults Nea rly Doubles in Just Two Years, http //www .gartner. com/ it/pag e.jsp3.
8 Gartner. "UK phishing fraud losses double" STAMFORD, Conn., (April 14, 2009). "Gartner Says Number of Phishing Attacks on U.S. Consumers Increased 40 Percent in 2008".. Finextra. March 7, 2006. http:// www. finextra. com/ fullstory asp?id=15013.
9 Jaco F. Schutte “The Particle Swarm Optimization Algorithm” EGM 6365 - Structural Optimization Fall 2005
10 L. Bianchi, L.M. Gambardella, M.Dorigo. An ant colony optimization approach to the probabilistic traveling salesman problem. In Proceedings of PPSN-VII, Seventh InterGARTNE R, INC.
11 M. E. Bergen, Technische Universität Berlin, Germany, 1995 Canstraint-based assembly line sequencing, Lecture Notes in Computer
12 Miller, Rich. "Bank, Customers Spar Over Phishing Losses". Netcraft. http://n ews.netcraft .com/ rchives/ 2006/09.
13 Mining Fuzzy Weighted Association Rules Proceedings of the 40th Hawaii International Conference on System Sciences – 2007.
14 Particle Swarm Optimization , www.swaminteLligence.org.
15 Particle Swarm Optimization, WIKI Pedia.
16 Richardson, Tim (May 3, 2005). "Brits fall prey to phishing". The Register. http:/ /www .theregister.co.uk/2005/05/03/aol_phishing/.
17 T.Moore and R. Clayton, "An empirical analysis of the current state of phishing attack and defence", In Proceedings of the Workshop on the Economics of Information Security (WEIS2007)
18 WEKA - University of Waikato, New Zealand, EN,2006: "Weka -Data Mining with Open Source Machine Learning Software in Java", 2006 ,
19 Xun Dong,”PSO Introduction” Department of Computer Science University of York, United Kingdom Email: xundong@cs.york.ac.uk,
Dr. Radha Damodaram
- India
Mr. M.L.Valarmathi
- India