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Paper Abstract and Keywords
Presentation 2020-11-26 14:00
Malware detection for IoT devices using whitelist and Isolation Forest
Masataka Nakahara, Norihiro Okui, Yasuaki Kobayashi, Yutaka Miyake (KDDI Research) ICSS2020-20
Abstract (in Japanese) (See Japanese page) 
(in English) As the number of IoT (Internet of Things) devices increases, the countermeasures against cyberattacks related to IoT devices become more important. Although methods to prevent malware infection to IoT devices are important, such prevention becomes difficult due to sophisticated infection steps and lack of computational resource for security software in IoT devices. Therefore, detecting malware infection of devices is also important to suppress malware spread. As the types of IoT devices and malwares are increasing, advanced anomaly detection method like machine learning is required to find malware infected devices. Because IoT devices cannot analyze own behavior by using machine learning due to limited computing resources, such analysis should be executed at gateway devices to the Internet. So we have proposed an architecture for detecting malware behavior using flow data of packets instead of whole packet information. As this proposal only uses flow information of each IoT device, it can reduce the storage space taken up by data and can analyze number of IoT devices with low computational resources. We performed the malware traffic detection on proposed architecture by using machine learning algorithms, but there were a lot of false positive detection. Therefore, in this paper, we propose hybrid system using machine learning and white list automatically generated from the packet of devices. The white list eliminated benign packets from the target of malware traffic detection, and it can decrease the false positive rate. We evaluate the performance of proposed method and show the efficiency.
Keyword (in Japanese) (See Japanese page) 
(in English) IoT / anomaly detection / machine learning / white list / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 264, ICSS2020-20, pp. 7-12, Nov. 2020.
Paper # ICSS2020-20 
Date of Issue 2020-11-19 (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 ICSS2020-20

Conference Information
Committee ICSS  
Conference Date 2020-11-26 - 2020-11-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Security, etc. 
Paper Information
Registration To ICSS 
Conference Code 2020-11-ICSS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Malware detection for IoT devices using whitelist and Isolation Forest 
Sub Title (in English)  
Keyword(1) IoT  
Keyword(2) anomaly detection  
Keyword(3) machine learning  
Keyword(4) white list  
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1st Author's Name Masataka Nakahara  
1st Author's Affiliation KDDI Research, Inc. (KDDI Research)
2nd Author's Name Norihiro Okui  
2nd Author's Affiliation KDDI Research, Inc. (KDDI Research)
3rd Author's Name Yasuaki Kobayashi  
3rd Author's Affiliation KDDI Research, Inc. (KDDI Research)
4th Author's Name Yutaka Miyake  
4th Author's Affiliation KDDI Research, Inc. (KDDI Research)
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Speaker Author-1 
Date Time 2020-11-26 14:00:00 
Presentation Time 25 minutes 
Registration for ICSS 
Paper # ICSS2020-20 
Volume (vol) vol.120 
Number (no) no.264 
Page pp.7-12 
#Pages 6 
Date of Issue 2020-11-19 (ICSS) 

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