CFP last date
15 April 2024
Reseach Article

Phishing Detection in E-mails using Machine Learning

by Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 7
Year of Publication: 2017
Authors: Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik
10.5120/ijais2017451713

Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik . Phishing Detection in E-mails using Machine Learning. International Journal of Applied Information Systems. 12, 7 ( October 2017), 21-24. DOI=10.5120/ijais2017451713

@article{ 10.5120/ijais2017451713,
author = { Srishti Rawal, Bhuvan Rawal, Aakhila Shaheen, Shubham Malik },
title = { Phishing Detection in E-mails using Machine Learning },
journal = { International Journal of Applied Information Systems },
issue_date = { October 2017 },
volume = { 12 },
number = { 7 },
month = { October },
year = { 2017 },
issn = { 2249-0868 },
pages = { 21-24 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number7/1005-2017451713/ },
doi = { 10.5120/ijais2017451713 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:08:21.605515+05:30
%A Srishti Rawal
%A Bhuvan Rawal
%A Aakhila Shaheen
%A Shubham Malik
%T Phishing Detection in E-mails using Machine Learning
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 7
%P 21-24
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Emails are widely used as a means of communication for personal and professional use. The information exchanged over mails is often sensitive and confidential such as banking information, credit reports, login details etc. This makes them valuable to cyber criminals who can use the information for malicious purposes. Phishing is a strategy used by fraudsters to obtain sensitive information from people by pretending to be from recognized sources. In a phished email, the sender can convince you to provide personal information under false pretenses. This experimentation considers the detection of a phished email as a classification problem and this paper describes the use of machine learning algorithms to classify emails as phished or ham. Maximum accuracy of 99. 87% is achieved in classification of emails using SVM and Random Forest classifier.

References
  1. Verizon, Data Breach Report 2016
  2. Andronicus A. Akinyelu and Aderemi O. Adewumi. Classification of Phishing Email using Random forest Machine Learning Technique 2014.
  3. Noor Ghazi M. Jameel, Loay E. George. Detection of Phishing Emails using Feed Forward Neural Network, International Journal of Computer Applications 2013.
  4. Ian Fette, Norman Sadeh, Anthony Tomasi, Learning to Detect Phishing Emails, In Proceedings of the International World Wide Web Conference (WWW), 2006
  5. Gilchan Park, Julia M. Taylor, Using Syntactic Features for Phishing Detection 2015, https://arxiv.org/ftp/arxiv/papers/1506/1506.00037.pdf
  6. Gori Mohamed .J, M. Mohammed Mohideen, Mrs. Shahira Banu. Email Phishing - An open threat to everyone, International Journal of Scientific Research Publications, 2014
  7. C. Emilin Shyni, S. Sarju, S. Swaminathan A Multi-Classifier Based Prediction Model for Phishing Emails Detection Using Topic Modelling, Named Entity Recognition and Image Processing, SciRes 2016
  8. Noor Ghazi M. Jamee , Loay E. George (2014), “Detection Phishing Emails Using Features Decisive Values”,257-259
  9. Rakesh M. Verma and Nirmala Rai. Phish-IDetector: Message-Id Based Automatic Phishing Detection, International Joint Conference on e-Business and Telecommunications 2015 .
  10. Basnet R., Mukkamala S., Sung A.H. (2008) Detection of Phishing Attacks: A Machine Learning Approach. In: Prasad B. (eds) Soft Computing Applications in Industry. Studies in Fuzziness and Soft Computing, vol 226. Springer, Berlin, Heidelberg
  11. Adwan Yasin and Adbelmunem, An intelligent classification model for phishing email detection , International Journal of Network Security & Its Applications (IJNSA) Vol.8, No.4, July 2016
  12. D. J. Hand, Heikki Mannila, Padhraic Smyth. Principles of Data Mining
  13. Ron Kohavi, A study of cross validation and bootstrap for accuracy estimation and model selection, International Joint Conference on Artificial Intelligence, 1995.
Index Terms

Computer Science
Information Sciences

Keywords

Phishing detection SVM ham naive bayes machine learning email fraud artificial intelligence