Biblio

This paper is the first to investigate the long-term stability of Surface Acoustic Wave Gyroscopes (SAWG) using an ovenized control system. Monolithic integration of a MEMS heater adjacent to SAW devices on Lithium Niobate over insulator substrate (LNOI) tightly couples frequency-based temperature detection with heating for temperature and frequency stabilization. This first prototype demonstrates the ability to minimize the temperature variations of the SAWG to below ±10 μK and stabilize the SAWG resonance frequency to ±0.2 ppm. This approach thus eliminates the thermal drift in a SAWG and enables the development of a new generation of MEMS-based gyroscopes with long-term stability.

We present a security analysis of several gesture CAPTCHA challenges designed to operate on mobiles. Mobile gesture CAPTCHA challenges utilize the accelerometer and the gyroscope inputs from a mobile to allow a human to solve a simple test by physically manipulating the device. We have evaluated the security of gesture CAPTCHA in mobile devices and found them resistant to a range of common automated attacks. Our study has shown that using an accelerometer and the gyroscope readings as an input to solve the CAPTCHA is difficult for malware, but easy for a real user. Gesture CAPTCHA is effective in differentiating between humans and machines.

We present novel CAPTCHA challenges based on user gestures designed for mobile. A gesture CAPTCHA challenge is a security mechanism to prevent malware from gaining access to network resources from mobile. Mobile devices contain a number of sensors that record the physical movement of the device. We utilized the accelerometer and gyroscope data as inputs to our novel CAPTCHAs to capture the physical manipulation of the device. We conducted an experimental study on a group of people. We discovered that younger people are able to solve this type of CAPTCHA challenges successfully in a short amount of time. We found that using accelerometer readings produces issues for some older people.

Smartwatches, with motion sensors, are becoming a common utility for users. With the increasing popularity of practical wearable computers, and in particular smartwatches, the security risks linked with sensors on board these devices have yet to be fully explored. Recent research literature has demonstrated the capability of using a smartphone's own accelerometer and gyroscope to infer tap locations; this paper expands on this work to demonstrate a method for inferring smartphone PINs through the analysis of smartwatch motion sensors. This study determines the feasibility and accuracy of inferring user keystrokes on a smartphone through a smartwatch worn by the user. Specifically, we show that with malware accessing only the smartwatch's motion sensors, it is possible to recognize user activity and specific numeric keypad entries. In a controlled scenario, we achieve results no less than 41% and up to 92% accurate for PIN prediction within 5 guesses.