Fuzzy pattern tree for edge malware detection and categorization in IoT
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...
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| Published in | Journal of systems architecture Vol. 97; pp. 1 - 7 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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Elsevier B.V
01.08.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. We 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, led to more powerful edge computing malware detection and categorization method. |
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| AbstractList | 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. We 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, led to more powerful edge computing malware detection and categorization method. |
| Author | Parizi, Reza M. Karimipour, Hadis Dovom, Ensieh Modiri Azmoodeh, Amin Dehghantanha, Ali Newton, David Ellis |
| Author_xml | – sequence: 1 givenname: Ensieh Modiri surname: Dovom fullname: Dovom, Ensieh Modiri organization: School of Engineering, Azad University of Mashhad, Iran – sequence: 2 givenname: Amin orcidid: 0000-0002-4109-4395 surname: Azmoodeh fullname: Azmoodeh, Amin organization: Cyber Science Lab, School of Computer Science, University of Guelph, Ontario, Canada – sequence: 3 givenname: Ali orcidid: 0000-0002-9294-7554 surname: Dehghantanha fullname: Dehghantanha, Ali email: ali@cybersciencelab.org organization: Cyber Science Lab, School of Computer Science, University of Guelph, Ontario, Canada – sequence: 4 givenname: David Ellis surname: Newton fullname: Newton, David Ellis organization: School of Computer Science, University of Salford, UK – sequence: 5 givenname: Reza M. surname: Parizi fullname: Parizi, Reza M. organization: Department of Software Engineering and Game Development, Kennesaw State University, GA, USA – sequence: 6 givenname: Hadis orcidid: 0000-0001-7948-4033 surname: Karimipour fullname: Karimipour, Hadis organization: School of Engineering, University of Guelph, Guelph, Canada |
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| Keywords | Fuzzy pattern tree Malware detection Cyber security Machine learning Edge computing IoT |
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