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Paper Abstract and Keywords
Presentation 2021-06-22 09:00
Feature analysis of phishing website and phishing detection based on machine learning algorithms
Yi Wei, Yuji Sekiya (Todai) IA2021-9 ICSS2021-9
Abstract (in Japanese) (See Japanese page) 
(in English) Phishing is a kind of cybercrime that uses disguised websites to trick people into providing personally sensitive information. Machine Learning is a branch of Artificial Intelligence (AI), which can learn from datasets and make predictions with minimal human intervention. Phishing detection is a typical binary classification task that can be predicted by machine learning algorithms. This paper analyzes 111 features of the latest phishing websites dataset，which includes 27998 legitimate websites and 30647 phishing websites to investigate the obvious differences and correlations between phishing and legitimate websites. Then, seven commonly used machine learning algorithms are compared to detect phishing websites, including Logistic Regression, Linear Discriminant Analysis, Classification and Regression Tree, Support Vector Machine (SVM), Naïve Bayes Classifier, Random Forest, and K-Nearest Neighbor. Among these algorithms, Random Forest shows the highest accuracy and the best performance.
Keyword (in Japanese) (See Japanese page) 
(in English) Cyber Security / Feature Analysis / Phishing Website Detection / Machine Learning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 69, ICSS2021-9, pp. 44-49, June 2021.
Paper # ICSS2021-9 
Date of Issue 2021-06-14 (IA, ICSS) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF IA2021-9 ICSS2021-9

Conference Information
Committee IA ICSS  
Conference Date 2021-06-21 - 2021-06-22 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet Security, etc. 
Paper Information
Registration To ICSS 
Conference Code 2021-06-IA-ICSS 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Feature analysis of phishing website and phishing detection based on machine learning algorithms 
Sub Title (in English)  
Keyword(1) Cyber Security  
Keyword(2) Feature Analysis  
Keyword(3) Phishing Website Detection  
Keyword(4) Machine Learning  
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1st Author's Name Yi Wei  
1st Author's Affiliation The University of Tokyo (Todai)
2nd Author's Name Yuji Sekiya  
2nd Author's Affiliation The University of Tokyo (Todai)
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Speaker Author-1 
Date Time 2021-06-22 09:00:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # IA2021-9, ICSS2021-9 
Volume (vol) vol.121 
Number (no) no.68(IA), no.69(ICSS) 
Page pp.44-49 
#Pages 6 
Date of Issue 2021-06-14 (IA, ICSS) 

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