Fuzzy pattern tree for edge malware detection and categorization in IoT

Dovom, EM, Azmoodeh, A, Dehghantanha, A ORCID: https://orcid.org/0000-0002-9294-7554, Newton, DE, Parizi, RM and Karimipour, H 2019, 'Fuzzy pattern tree for edge malware detection and categorization in IoT' , Journal of Systems Architecture, 97 (Aug 19) , pp. 1-7.

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The surging pace of Internet of Things (IoT) development and its applications has resulted in significantly large amounts of data (commonly known as big data) being communicated and processed across IoT networks. While cloud computing has led to several possibilities in regard to this computational challenge, there are several security risks and concerns associated with it. Edge computing is a state-of-the-art subject in IoT that attempts to decentralize, distribute and transfer computation to IoT nodes. Furthermore, IoT nodes that perform applications are the primary target vectors which allow cybercriminals to threaten an IoT network. Hence, providing applied and robust methods to detect malicious activities by nodes is a big step to protect all of the network.

In this study, we transmute the programs' OpCodes into a vector space and employ fuzzy and fast fuzzy pattern tree methods for malware detection and categorization and obtained a high degree of accuracy during reasonable run-times especially for the fast fuzzy pattern tree. Both utilized feature extraction and fuzzy classification which were robust and led to more powerful edge computing malware detection and categorization method.

Item Type: Article
Schools: Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title: Journal of Systems Architecture
Publisher: Elsevier
ISSN: 1383-7621
Depositing User: DE Newton
Date Deposited: 29 Mar 2019 09:04
Last Modified: 28 Aug 2021 13:55
URI: http://usir.salford.ac.uk/id/eprint/50637

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