Phishing Website Detection using Supervised Deep Learning 
L Lakshmi1, K Pushpa Rani2, K Vijay3, G.V.S.S Priyanka4
1L Lakshmi, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
2K Pushpa Rani, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad (Telangana), India
3K Vijay3, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
4G.V.S.S Priyanka, Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad (Telangana), India.
Manuscript received on November 12, 2019. | Revised Manuscript received on November 25, 2019. | Manuscript published on 30 November, 2019. | PP: 4844-4846 | Volume-8 Issue-4, November 2019. | Retrieval Number: D8016118419/2019©BEIESP | DOI: 10.35940/ijrte.D8016.118419
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© 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: The website phishing is the tremendously growing problem over the internet which will lead to the loss of personal information. This process will run like, when ever user clicks a website link it will lead them to the web page that is created by the phisher to deceive the user. After this phishing has been started in order to stop it many techniques came into existence to detect the phished web site and help the user from being deceived by the attacker. Even though many techniques have adapted to stop the attackers, it is difficult because as many phished web pages are generated by the attackers within few hours. Most of the techniques to detect these phishing websites are not able to decide the fake website with legitimate one because the accuracy of getting results are much less. There are many supervised machine learning techniques which are supervised, where a primary set of data is given to the algorithm and depending on that set the algorithm will be trained and it will predict the results for the same. One of the most important techniques that is deep Learning classifiers is applied with significant features to detect phishing websites. By using this algorithm we can classify the phishing websites from genuine websites by using effective features. In this algorithmic approach to detect genuine websites a feature set is used so by analyzing these features using deep neural networks we can detect a website is phished or not. We can also increase the accuracy of our algorithm by adding certain more features and increasing the hidden layers in neural networks.
Keywords: Deep Learning, Feature Set, Supervised Learning, Phishing.
Scope of the Article: Deep Learning.