Analysis on interference between Multi-Giga bit display serial link and RF components in Smart Mobile Device

In an era of tremendous development in information technology and the Internet of Things (IoT), security plays a key role in safety devices connected with the Internet. Authentication is vital in the security field, and to achieve a strong authentication scheme, there are several systems using a Multi-Factor Authentication (MFA) scheme based on a smart card, token, and biometric. However, these schemes have suffered from the extra cost; lost, stolen or broken factor, and malicious attacks. In this paper, we design an MFA protocol to be the authenticated administrator of IoT’s devices. The main components of our protocol are a smart mobile device and the fuzzy extractor of the administrator’s fingerprint. The information of the authenticated user is stored in an anomalous manner in mobile devices and servers to resist well-known attacks, and, as a result, the attacker fails to authenticate the system when they obtain a mobile device or password. Our work overcomes the above-mentioned issues and does not require extra cost for a fingerprint device. By using the AVISPA tool to analysis protocol security, the results are good and safe against known attacks.

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Integrating Gaze Tracking and Head-Motion Prediction for Mobile Device Authentication: A Proof of Concept

Sensors ◽

10.3390/s18092894 ◽

2018 ◽

Vol 18 (9) ◽

pp. 2894 ◽

Cited By ~ 5

Author(s):

Zhuo Ma ◽

Xinglong Wang ◽

Ruijie Ma ◽

Zhuzhu Wang ◽

Jianfeng Ma

Keyword(s):

Mobile Device ◽

Support Vector ◽

Svm Classifier ◽

Dynamic Features ◽

Gaze Tracking ◽

The Gaze ◽

Shoulder Surfing ◽

Static Feature ◽

Smart Mobile ◽

Stream Model

We introduce a two-stream model to use reflexive eye movements for smart mobile device authentication. Our model is based on two pre-trained neural networks, iTracker and PredNet, targeting two independent tasks: (i) gaze tracking and (ii) future frame prediction. We design a procedure to randomly generate the visual stimulus on the screen of mobile device, and the frontal camera will simultaneously capture head motions of the user as one watches it. Then, iTracker calculates the gaze-coordinates error which is treated as a static feature. To solve the imprecise gaze-coordinates caused by the low resolution of the frontal camera, we further take advantage of PredNet to extract the dynamic features between consecutive frames. In order to resist traditional attacks (shoulder surfing and impersonation attacks) during the procedure of mobile device authentication, we innovatively combine static features and dynamic features to train a 2-class support vector machine (SVM) classifier. The experiment results show that the classifier achieves accuracy of 98.6% to authenticate the user identity of mobile devices.

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