Domain adaptation based machine learning transferability model

dc.contributor.advisorGhorbani, Ali A.
dc.contributor.advisorIsah, Haruna
dc.contributor.authorMolokwu, Reginald Chukwuka
dc.date.accessioned2024-07-31T18:43:34Z
dc.date.available2024-07-31T18:43:34Z
dc.date.issued2024-06
dc.description.abstractIn this study, we tackle the pressing issue of accurately identifying IoT devices across a variety of operational environments. With the ever-changing and dynamic nature of IoT ecosystems at the forefront, we introduce an innovative approach for device identification. Our solution hinges on the power of machine learning models’ transferability, enhanced by domain adaptation techniques. These techniques are key in addressing the inconsistencies present across different IoT settings. Our method involves a detailed examination of network packets to extract essential features, which are then utilized in machine learning algorithms to ensure precise device identification despite potential domain shifts and class imbalances. Leveraging this approach, we employ datasets such as IMC’19, CICIoT 2022, and Sentinel to test and validate our module. The outcome is noteworthy; we achieve a significant 98% accuracy in both testing and evaluation phases, demonstrating the effectiveness of our method in the complex landscape of IoT device identification.
dc.description.copyright© Reginald Chukwuka Molokwu, 2024
dc.format.extentxii, 90
dc.format.mediumelectronic
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/38072
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.titleDomain adaptation based machine learning transferability model
dc.typemaster thesis
oaire.license.conditionother
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

Files

Original bundle
Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
Reginald Chukwuka Molokwu - Thesis.pdf
Size:
3.68 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.13 KB
Format:
Item-specific license agreed upon to submission
Description: