Versatile Authentication Using an Identifier Based On Optical Variable Nanostructures

Patel, Devarshi; Jiang, Hao; Patel, Jasbir N.; Kaminska, Bożena

doi:10.1109/tnano.2019.2943298

Cited by 2 publications

(2 citation statements)

References 18 publications

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“…The major user authentication technologies include; password authentication method, a one-time password (OTP) [24], and biometric authentication using iris and fingerprint [25]. Among them, the password authentication is most widely used due to its ease of deployment [3]. However, when authentication information such as ID and password are exposed to an attacker, the attacker takes over the server, which in turn poses a threat to the entire IoT [4].…”

Section: Introductionmentioning

confidence: 99%

“…Although password authentication is an old method, it is widely used, due to its easy-to-deploy advantages [2]. In IoT, most PC platforms, such as servers, use password authentication [3]. For this reason, if a user password is exposed, core systems in IoT are taken over by an adversary, causing a serious problem that may lead to malfunction of control systems or sensors [4].…”

Section: Introductionmentioning

confidence: 99%

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Offensive Security of Keyboard Data Using Machine Learning for Password Authentication in IoT

Lee

Choi

et al. 2021

IEEE Access

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In this paper, to increase the attack success rate, we analyzed the distribution of all collected keyboard data based on the distance of time and keyboard scancode data, which presents the crucial data from the previous study. To achieve this, we derived time-distance based features that have higher attack success rates than in previous studies. The proposed attack method defines 6 features, and evaluates the performance based on 18 datasets. For performance evaluation, the accuracy, precision, recall, F1-score, and AUC of Datasets (1 to 3) were compared, and two experiments showed improved overall performance by at least 10.6 % and up to 16.1 % compared to previous studies in terms of the performance evaluation for each feature, comparison of variations in maximum performance, comparison of variations in performance of each feature, and comparison of variations in overall performance. Moreover, the best accuracy, which represents the probability of password exposure, was 96.7 %, which suggests that our proposed attack method has a higher accuracy than the previous study (96.2 %). In conclusion, we demonstrated that password authentication is neutralized by stealing the user password more effectively. For future research, we will focus on improving the attack success rate with respect to accuracy and overall performance numbers, using not only machine learning, but also deep learning.

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