Finding Fraud Websites by Using Data Mining Techniques

Authors(2) :- Maheswari G, Madhura P

Globally the internet is been accessed by huge amount people inside their restricted domains. once the client and server exchange messages among one another, there's an activity that may be observed in log files. Log files provides a elaborated description of the activities that occur in an exceedingly network that shows the IP address, login and logout durations, the userís behavior etc. There are many varieties of attacks occurring from the net. Our focus of analysis is on Denial of Service (DOS) attacks with the help of pattern recognition techniques in data processing. Through that the Denial of Service attack is known. Denial of service is a terribly dangerous attack that jeopardizes the IT resources of a corporation by overloading with imitation messages or multiple requests from unauthorized users. But we cannot detect the fake website in this criteria. In order to detect and predict e-banking phishing website, we proposed an intelligent, flexible and effective system that is based on using classification Data mining algorithm. We implemented classification algorithm and techniques to extract the phishing data sets criteria to classify their legitimacy.

Authors and Affiliations

Maheswari G
Department of Computer Applications, Rayalaseema Institute of Information and Management Sciences, Tirupathi, Andra Pradesh, India
Madhura P
Department of Computer Applications, Rayalaseema Institute of Information and Management Sciences, Tirupathi, Andra Pradesh, India

Phishing websites, DOS attacks, Data mining, Association rules, cluster analysis, Log File, Cyber Crimes.

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Publication Details

Published in : Volume 3 | Issue 4 | March-April 2018
Date of Publication : 2018-03-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 1195-1198
Manuscript Number : CSEIT1833582
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Maheswari G, Madhura P, "Finding Fraud Websites by Using Data Mining Techniques", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 4, pp.1195-1198, March-April-2018.
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