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Paper Abstract and Keywords
Presentation 2020-10-01 11:15
Malicious URLs Detection Using an Integrated AI Framework
Bo-Xiang Wang, Ren-Feng Deng, Yi-Wei Ma, Jiann-Liang Chen (NTUST) IA2020-1
Abstract (in Japanese) (See Japanese page) 
(in English) Malicious attacks on computer networks are quite common, and the internet attacks are even more widespread, such as Malvertising, Phishing, and Drive-by download, all of which are related to malicious URL links. The conventional way to prevent these malicious URLs would be to manage them through a blacklist, that requires considerable human resources to identify them. In recent years, with the improvement of hardware and software devices, computers with machine learning are able to learn and predict from large amounts of data, therefor replacing traditional methods and saving manpower. This study proposed an integrated AI framework, which consists of a fast filtering component and a precise identification component. This framework combines the advantages of the CNN (Convolutional Neural Network) model and the XGBoost (eXtreme Gradient Boosting) model to achieve a fast and accurate detection capability. Experimental results show that the fast filter is able to detect results in 0.6 seconds with an accuracy of 83%. In contrast, the accuracy of the precision identification component is 94% when it takes about 40 seconds to detect the result. This study integrates the advantages of the two components to achieve the goal of fast and accurate malicious URL detection.
Keyword (in Japanese) (See Japanese page) 
(in English) Malicious URL / Integrated AI framework / Artificial Intelligence / Feature Selection / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 177, IA2020-1, pp. 1-5, Oct. 2020.
Paper # IA2020-1 
Date of Issue 2020-09-24 (IA) 
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)
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Conference Information
Committee IA  
Conference Date 2020-10-01 - 2020-10-01 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) IA2020 - Workshop on Internet Architecture and Applications 2020 
Paper Information
Registration To IA 
Conference Code 2020-10-IA 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Malicious URLs Detection Using an Integrated AI Framework 
Sub Title (in English)  
Keyword(1) Malicious URL  
Keyword(2) Integrated AI framework  
Keyword(3) Artificial Intelligence  
Keyword(4) Feature Selection  
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1st Author's Name Bo-Xiang Wang  
1st Author's Affiliation National Taiwan University of Science and Technology (NTUST)
2nd Author's Name Ren-Feng Deng  
2nd Author's Affiliation National Taiwan University of Science and Technology (NTUST)
3rd Author's Name Yi-Wei Ma  
3rd Author's Affiliation National Taiwan University of Science and Technology (NTUST)
4th Author's Name Jiann-Liang Chen  
4th Author's Affiliation National Taiwan University of Science and Technology (NTUST)
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Speaker Author-1 
Date Time 2020-10-01 11:15:00 
Presentation Time 25 minutes 
Registration for IA 
Paper # IA2020-1 
Volume (vol) vol.120 
Number (no) no.177 
Page pp.1-5 
#Pages 5 
Date of Issue 2020-09-24 (IA) 

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