A Machine Learning Approach for Detection of Phished Websites using Neural Networks
K. Selvan1, M. Vanitha2
1K. Selvan, Research Scholar, Department of Computer Science, JJ College of Arts and Science (Autonomous), Pudukkottai, (Tamil Nadu), India.
2Dr. M. Vanitha, Research Guide, Head, Department of Information Technology, JJ College of Arts and Science (Autonomous), Pudukkottai, (Tamil Nadu), India.
Manuscript received on 20 January 2016 | Revised Manuscript received on 30 January 2016 | Manuscript published on 30 January 2016 | PP: 19-23 | Volume-4 Issue-6, January 2016 | Retrieval Number: F1515014616©BEIESP
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Phishing is a means of obtaining confidential information through fraudulent website that appear to be legitimate On detection of all the criteria ambiguities and certain considerations involve hence neural network techniques are used to build an effective tool in identifying phished websites There are many phishing detection techniques available, but a central problem is that web browsers rely on a black list of known phishing website, but some phishing website has a lifespan as short as a few hours. These website with a shorter lifespan are known as zero day phishing website. Thus, a faster recognition system needs to be developed for the web browser to identify zero day phishing website .To develop a faster recognition system, a neural network technique is used which reduces the error and increases the performance. This paper describes a framework to better classify and predict the phishing sites.
Keywords: Detection, Machine Learning, Neural Network, Phishing, Security.
Scope of the Article: Machine Learning