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Robust Device Authentication Using Non-Standard Classification Features

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)
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: https://orcid.org/0000-0001-5590-0880
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