On the Improvements of Mouse Dynamics Based Continuous User Authentication

Hayri Durmaz*, Mehmet Keskinoz

*Bu çalışma için yazışmadan sorumlu yazar

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Özet

Traditional authentication methods are vulnerable when users leave their devices unattended or if their credentials are compromised. In contrast, continuous authentication offers a perpetual strategy for user validation, ensuring that only authorized users access critical information throughout their entire usage. The problem of continuous authentication boils down to a binary classification task: determining whether the usage is legal or illegal. Deep learning presents a promising solution for this problem, although the use of convolutional neural networks (CNNs) in continuous authentication still has room for improvement. In this study, we employ residual learning to train and test a user authentication model. To further enhance the accuracy of the results, we implement a realistic augmentation method and employ a superior image mapping technique compared to existing literature. As a result, we achieve significantly more accurate results than those reported in the referenced studies. On average, our tests yield a False Accept Rate of 0.45 and a False Reject Rate of 0.34, which are 6.5 times better than the referenced studies. These findings demonstrate a substantial improvement in the usability and effectiveness of real-world cybersecurity applications.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıProceedings - 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science, IRI 2023
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar13-19
Sayfa sayısı7
ISBN (Elektronik)9798350334586
DOI'lar
Yayın durumuYayınlandı - 2023
Harici olarak yayınlandıEvet
Etkinlik24th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2023 - Bellevue, United States
Süre: 4 Ağu 20236 Ağu 2023

Yayın serisi

AdıProceedings - 2023 IEEE 24th International Conference on Information Reuse and Integration for Data Science, IRI 2023

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???event.eventtypes.event.conference???24th IEEE International Conference on Information Reuse and Integration for Data Science, IRI 2023
Ülke/BölgeUnited States
ŞehirBellevue
Periyot4/08/236/08/23

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Publisher Copyright:
© 2023 IEEE.

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