Yadav, Supriya, Khanna, Pooja, Howells, Gareth (2021) Robust Device Authentication Using Non-Standard Classification Features. In: Proceedings International Conference for Internet Technology and Secured Transactions2021. . (In press) (KAR id:90750)
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Abstract
This paper investigates the use of novel hardware features derived from the physical and behavioral characteristics of electronic devices to identify such devices uniquely. Importantly, the features examined exhibit non-standard and multimodal distributions which present a significant challenge to model and characterize. Specifically, the potency of four data classification methods is compared whilst employing such characteristics, proposed model Multivariate Gaussian Distribution (MVGD -address multimodality), Logistic Regression (LogR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM). Performance is measured based on its accuracy, precision, recall and f measure. The experimental results reveal that by addressing multimodal features with proposed model Multivariate Gaussian Distribution classifier, the overall performance is better than the other classifiers.
Item Type: | Conference or workshop item (Paper) |
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Uncontrolled keywords: | Security, ICMetric, Authentication, Classifiers, Key generation, Multidimensional space |
Subjects: |
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, T Technology > TA Engineering (General). Civil engineering (General) > TA1637 Image processing |
Divisions: |
Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Engineering and Digital Arts |
Depositing User: | Gareth Howells |
Date Deposited: | 08 Oct 2021 16:43 UTC |
Last Modified: | 08 Oct 2021 16:43 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/90750 (The current URI for this page, for reference purposes) |
Howells, Gareth: | ![]() |
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