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| 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 |
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| 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) | |
| Download PDF | IA2020-1 |
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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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