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Network traffic analysis for threats detection in the Internet of Things
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Hammoudeh, M., Pimlott, J., Belguith, S., Epiphaniou, Gregory, Baker, T., Kayes, A. S. M., Adebisi, B. and Bounceur, A. (2020) Network traffic analysis for threats detection in the Internet of Things. IEEE Internet of Things Magazine, 3 (4). 40 -45. doi:10.1109/IOTM.0001.2000015 ISSN 2576-3180.
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Official URL: https://doi.org/10.1109/IOTM.0001.2000015
Abstract
As the prevalence of the Internet of Things (IoT) continues to increase, cyber criminals are quick to exploit the security gaps that many devices are inherently designed with. Users cannot be expected to tackle this threat alone, and many current solutions available for network monitoring are simply not accessible or can be difficult to implement for the average user, which is a gap that needs to be addressed. This article presents an effective signature-based solution to monitor, analyze, and detect potentially malicious traffic for IoT ecosystems in the typical home network environment by utilizing passive network sniffing techniques and a cloud application to monitor anomalous activity. The proposed solution focuses on two attack and propagation vectors leveraged by the infamous Mirai botnet, namely DNS and Telnet. Experimental evaluation demonstrates the proposed solution can detect 98.35 percent of malicious DNS traffic and 99.33 percent of Telnet traffic for an overall detection accuracy of 98.84 percent.
Item Type: | Journal Article | ||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering | ||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | Internet of things, Internet of things -- Security measures, Computer networks -- Security measures, Internet domain names | ||||||||
Journal or Publication Title: | IEEE Internet of Things Magazine | ||||||||
Publisher: | IEEE | ||||||||
ISSN: | 2576-3180 | ||||||||
Official Date: | December 2020 | ||||||||
Dates: |
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Volume: | 3 | ||||||||
Number: | 4 | ||||||||
Page Range: | 40 -45 | ||||||||
DOI: | 10.1109/IOTM.0001.2000015 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 11 May 2020 | ||||||||
Date of first compliant Open Access: | 12 May 2020 | ||||||||
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