Biblio

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.

Wearables are now ubiquitous items equipped with a multitude of sensors such as GPS, accelerometer, or Bluetooth. The raw data from this sensors are typically used in a health context. However, we can also use it for security purposes. In this paper, we present a solution that aims at using data from the sensors of a wearable device to identify the password a user is typing on a keyboard by using machine learning algorithms. Hence, the purpose is to determine whether a malicious third party application could extract sensitive data through the raw data that it has access to.

Heart rate monitoring has become increasingly popular in the industry through mobile phones and wearable devices. However, current determination of heart rate through mobile applications suffers from high corruption of signals during intensive physical exercise. In this paper, we present a novel technique for accurately determining heart rate during intensive motion by classifying PPG signals obtained from smartphones or wearable devices combined with motion data obtained from accelerometer sensors. Our approach utilizes the Internet of Things (IoT) cloud connectivity of smartphones for selection of PPG signals using deep learning. The technique is validated using the TROIKA dataset and is accurately able to predict heart rate with a 10-fold cross validation error margin of 4.88%.

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.