Paper Abstract and Keywords |
Presentation |
2019-11-15 13:15
Malicious URL Classification using Machine Learning Techniques Yu-Chen Chen, Li-Dong Chen, Yan-Ju Chen, Jiann-Liang Chen (NTUST) IA2019-41 |
Abstract |
(in Japanese) |
(See Japanese page) |
(in English) |
The Website security is an important research topic that must be pursued to protect internet users. Traditionally, blacklists of malicious websites are maintained, but they do not help in the detection of new malicious websites. This work proposes a machine learning architecture for detecting malicious URLs Forty-one features of malicious URLs are extracted using Domain, Alexa and Obfuscation Technique. ANOVA and XGBoost are used to identify the 17 most important features. Finally, dataset is used to train the XGBoost classifier, which has a classification accuracy of more than 99% and high efficiency. |
Keyword |
(in Japanese) |
(See Japanese page) |
(in English) |
Malicious URL / Obfuscation techniques / JavaScript detection / Artificial Intelligence / Feature selection / / / |
Reference Info. |
IEICE Tech. Rep., vol. 119, no. 291, IA2019-41, pp. 79-83, Nov. 2019. |
Paper # |
IA2019-41 |
Date of Issue |
2019-11-07 (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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IA2019-41 |
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