Anti-phishing System using LSTM and CNN

Yazhmozhi V M Vasuki Murugesan (Lead / Corresponding author), Janet B, Srinivasulu Reddy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Users prefer to do e-banking and e-shopping now-a-days because of the exponential growth of the internet. Because of this paradigm shift, hackers are finding umpteen ways to steal our personal information and critical details like
details of debit and credit cards, by disguising themselves as reputed websites, just by changing the spelling or making minor modifications to the URL. Identifying whether an URL is benign or malicious is a challenging job, because it makes use of the weakness of the user. While there are several works carried out to detect phishing websites, they only use heuristic methods and list based techniques and therefore couldn’t avoid phishing effectively. In this paper an anti-phishing system was proposed to protect the users. It uses an ensemble model that uses both LSTM and CNN with a massive data set containing nearly 2,00,000 URLs, that is balanced. After analyzing the accuracy of different existing approaches, it has been found that the ensemble model that uses both LSTM and CNN performed better with an accuracy of 96% and the precision is 97% respectively which is far better than the existing solutions.
Original languageEnglish
Title of host publication2020 IEEE International Conference for Innovation in Technology (INOCON)
Place of PublicationBangluru
PublisherIEEE
Number of pages5
ISBN (Electronic)978-1-7281-9744-9
ISBN (Print)978-1-7281-9745-6
DOIs
Publication statusPublished - 1 Jan 2021
Event2020 IEEE International Conference for Innovation in Technology - Bangluru, India
Duration: 6 Nov 20208 Nov 2020
http://inoconf.org/

Conference

Conference2020 IEEE International Conference for Innovation in Technology
Abbreviated titleINOCON 2020
Country/TerritoryIndia
CityBangluru
Period6/11/208/11/20
Internet address

Keywords

  • Phishing
  • CNN
  • RNN
  • Deep Learning
  • Classification
  • Security

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