Extreme Learning Model Based Phishing Classifier
Praveen Tumuluru1, Radha Manohar Jonnalagadda2, Divya Sai Sree Konatham3, Vineetha Samineni4, Burra Lakshmi Ramani5
1Praveen Tumuluru Assistant professor, Department of CSE, Koneru Lakshmaiah College of Engineering, KLEF, Guntur, Andhra Pradesh, India.
2Radha Manohar Jonnalagadda, Student, Department of CSE, Bachelor of Technology, Koneru Lakshmiah College of Engineering, KLEF, Andhra Pradesh, India.
3Divya Sai Sree Konatham Student, Department of CSE, Bachelor of Technology, Koneru Lakshmiah College of Engineering, KLEF, Andhra Pradesh, India.
4Vineetha Samineni Student, Department of CSE, Bachelor of Technology, Koneru Lakshmiah College of Engineering, KLEF, Andhra Pradesh, India.
5Burra Lakshmi Ramani, Asst professor, Computer Science and Engineering Dept., PVP Siddhartha Institute of Technology, Kanuru, Vijayawada, India.
Manuscript received on November 19, 2019. | Revised Manuscript received on November 29 2019. | Manuscript published on 30 November, 2019. | PP: 9606-9612 | Volume-8 Issue-4, November 2019. | Retrieval Number: D9984118419/2019©BEIESP | DOI: 10.35940/ijrte.D9984.118419
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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 An Act of Attempting To Acquire The Users’ Data Such As Usernames, Passwords And Credit Card Details As It Was The Trustworthy Entity In An Electronic Communication. Because of The Quick Development of The Internet, Clients Change Their Inclination From Customary Shopping To The Electronic Business. Rather Than Bank Or Shop Robbery, These Days Culprit Attempt To Discover Their Victims In The Internet With Some Particular Tricks. By Utilizing The Mysterious Structure of The Internet, The Culprits Set Out New Strategies, For Example, Phishing, To Betray Victims With The Utilization Of Fake Webpages To Gather Their Delicate Data, For Example, Account Ids, Usernames, Passwords, And So On. Understanding Whether A Website Page Is Genuine Or Phishing Is A Very Testing Issue, Because Of Its Semantics-Based Assault Structure, Which Predominantly Misuses The Pc Users’ Susceptibilities. Despite The Fact That Most Of The Software Companies Introduce Many Anti-Phishing Products, Which Use Blacklist Generator, Heuristic Approach And Ml-Based Methodologies, These Products Can’t Stop All Of The Phishing Attacks. The Main Objective Of This Paper Is To Find An Efficient Approach For Distinguishing The Phishing Sites Which Depends On The Extreme Learning Model. Specifically, The Proposed Method Computes Some Features As Input And Checks Whether The Given Url Is Phishing Url Or The Legitimate Url. The Proposed Extreme Learning Model Attains 97% Accuracy Rate For Detection Of Phishing Urls And If The Hidden Layers Increases The Accuracy Is Also Discussed.
Keywords: Phishing Classification, Url, Neural Networks, Extreme Learning Model, Classification, Back Propagation Algorithm.
Scope of the Article: Classification.