Graph-based IoT malware family classification

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dc.contributor.advisorGhorbani, Ali
dc.contributor.advisorLashkari, Arash Habibi
dc.contributor.authorMahmoudyar, Nastaran
dc.date.accessioned2023-03-01T16:18:18Z
dc.date.available2023-03-01T16:18:18Z
dc.date.issued2021
dc.date.updated2023-03-01T15:01:27Z
dc.description.abstractInternet of Things malware has become one of the main cyber-threats nowadays. There is no comprehensive study in a feature-based manner for IoT malware detection approaches to the best of our knowledge. Moreover, the studies show that there is a lack of IoT malware family classification system. This thesis attempts to bridge these gaps by proposing a feature-based IoT malware taxonomy and a graph-based IoT malware family classification framework by combining the FCGs and fuzzy hashes. We introduce the Aggregated Weighted Graph (AWGH) of Hashes, representing each IoT malware family's structure. We use IDA Pro [60] for generating the FCGs, ssdeep [3] for computing the fuzzy hashes, and Python for developing the fully automated framework. To evaluate the system's effectiveness, we use the VirusTotal dataset [4] and provide a comparative analysis with different IoT malware regarding their CPU architectures (MIPS, ARM, i386, PowerPC, and AMD64). The results show the effectiveness of our framework.
dc.description.copyright© Nastaran Mahmoudyar, 2021
dc.formattext/xml
dc.format.extentxiii, 116 pages
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/13415
dc.language.isoen_CA
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.titleGraph-based IoT malware family classification
dc.typemaster thesis
thesis.degree.disciplineComputer Science
thesis.degree.fullnameMaster of Computer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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