skip to main content

Title: Analysis of head and torso movements for authentication
Wearable computing devices have become increasingly popular and while these devices promise to improve our lives, they come with new challenges. One such device is the Google Glass from which data can be stolen easily as the touch gestures can be intercepted from a head-mounted device. This paper focuses on analyzing and combining two behavioral metrics, namely, head movement (captured through glass) and torso movement (captured through smartphone) to build a continuous authentication system that can be used on Google Glass alone or by pairing it with a smartphone. We performed a correlation analysis among the features on these two metrics and found that very little correlation exists between the features extracted from head and torso movements in most scenarios (set of activities). This led us to combine the two metrics to perform authentication. We built an authentication system using these metrics and compared the performance among different scenarios. We got EER less than 6% when authenticating a user using only the head movements in one scenario whereas the EER is less than 5% when authenticating a user using both head and torso movements in general.
Authors:
; ;
Award ID(s):
1527795
Publication Date:
NSF-PAR ID:
10068513
Journal Name:
IEEE 4th International Conference on Identity, Security, and Behavior Analysis (ISBA)
Sponsoring Org:
National Science Foundation
More Like this
  1. Smartphones are the most commonly used computing platform for accessing sensitive and important information placed on the Internet. Authenticating the smartphone's identity in addition to the user's identity is a widely adopted security augmentation method since conventional user authentication methods, such as password entry, often fail to provide strong protection by itself. In this paper, we propose a sensor-based device fingerprinting technique for identifying and authenticating individual mobile devices. Our technique, called MicPrint, exploits the unique characteristics of embedded microphones in mobile devices due to manufacturing variations in order to uniquely identify each device. Unlike conventional sensor-based device fingerprinting thatmore »are prone to spoofing attack via malware, MicPrint is fundamentally spoof-resistant since it uses acoustic features that are prominent only when the user blocks the microphone hole. This simple user intervention acts as implicit permission to fingerprint the sensor and can effectively prevent unauthorized fingerprinting using malware. We implement MicPrint on Google Pixel 1 and Samsung Nexus to evaluate the accuracy of device identification. We also evaluate its security against simple raw data attacks and sophisticated impersonation attacks. The results show that after several incremental training cycles under various environmental noises, MicPrint can achieve high accuracy and reliability for both smartphone models.« less
  2. Reliably identifying and authenticating smartphones is critical in our daily life since they are increasingly being used to manage sensitive data such as private messages and financial data. Recent researches on hardware fingerprinting show that each smartphone, regardless of the manufacturer or make, possesses a variety of hardware fingerprints that are unique, robust, and physically unclonable. There is a growing interest in designing and implementing hardware-rooted smartphone authentication which authenticates smartphones through verifying the hardware fingerprints of their built-in sensors. Unfortunately, previous fingerprinting methods either involve large registration overhead or suffer from fingerprint forgery attacks, rendering them infeasible in authenticationmore »systems. In this paper, we propose ABC, a real-time smartphone Authentication protocol utilizing the photo-response non-uniformity (PRNU) of the Built-in Camera. In contrast to previous works that require tens of images to build reliable PRNU features for conventional cameras, we are the first to observe that one image alone can uniquely identify a smartphone due to the unique PRNU of a smartphone image sensor. This new discovery makes the use of PRNU practical for smartphone authentication. While most existing hardware fingerprints are vulnerable against forgery attacks, ABC defeats forgery attacks by verifying a smartphone’s PRNU identity through a challenge response protocol using a visible light communication channel. A user captures two time-variant QR codes and sends the two images to a server, which verifies the identity by fingerprint and image content matching. The time-variant QR codes can also defeat replay attacks. Our experiments with 16,000 images over 40 smartphones show that ABC can efficiently authenticate user devices with an error rate less than 0.5%.« less
  3. Reliably identifying and authenticating smart- phones is critical in our daily life since they are increasingly being used to manage sensitive data such as private messages and financial data. Recent researches on hardware fingerprinting show that each smartphone, regardless of the manufacturer or make, possesses a variety of hardware fingerprints that are unique, robust, and physically unclonable. There is a growing interest in designing and implementing hardware-rooted smart- phone authentication which authenticates smartphones through verifying the hardware fingerprints of their built-in sensors. Unfortunately, previous fingerprinting methods either involve large registration overhead or suffer from fingerprint forgery attacks, rendering them infeasiblemore »in authentication systems. In this paper, we propose ABC, a real-time smartphone Au- thentication protocol utilizing the photo-response non-uniformity (PRNU) of the Built-in Camera. In contrast to previous works that require tens of images to build reliable PRNU features for conventional cameras, we are the first to observe that one image alone can uniquely identify a smartphone due to the unique PRNU of a smartphone image sensor. This new discovery makes the use of PRNU practical for smartphone authentication. While most existing hardware fingerprints are vulnerable against forgery attacks, ABC defeats forgery attacks by verifying a smartphone’s PRNU identity through a challenge response protocol using a visible light communication channel. A user captures two time-variant QR codes and sends the two images to a server, which verifies the identity by fingerprint and image content matching. The time-variant QR codes can also defeat replay attacks. Our experiments with 16,000 images over 40 smartphones show that ABC can efficiently authenticate user devices with an error rate less than 0.5%.« less
  4. Mobile devices typically rely on entry-point and other one-time authentication mechanisms such as a password, PIN, fingerprint, iris, or face. But these authentication types are prone to a wide attack vector and worse 1 INTRODUCTION Currently smartphones are predominantly protected a patterned password is prone to smudge attacks, and fingerprint scanning is prone to spoof attacks. Other forms of attacks include video capture and shoulder surfing. Given the increasingly important roles smartphones play in e-commerce and other operations where security is crucial, there lies a strong need of continuous authentication mechanisms to complement and enhance one-time authentication such that evenmore »if the authentication at the point of login gets compromised, the device is still unobtrusively protected by additional security measures in a continuous fashion. The research community has investigated several continuous authentication mechanisms based on unique human behavioral traits, including typing, swiping, and gait. To this end, we focus on investigating physiological traits. While interacting with hand-held devices, individuals strive to achieve stability and precision. This is because a certain degree of stability is required in order to manipulate and interact successfully with smartphones, while precision is needed for tasks such as touching or tapping a small target on the touch screen (Sitov´a et al., 2015). As a result, to achieve stability and precision, individuals tend to develop their own postural preferences, such as holding a phone with one or both hands, supporting hands on the sides of upper torso and interacting, keeping the phone on the table and typing with the preferred finger, setting the phone on knees while sitting crosslegged and typing, supporting both elbows on chair handles and typing. On the other hand, physiological traits, such as hand-size, grip strength, muscles, age, 424 Ray, A., Hou, D., Schuckers, S. and Barbir, A. Continuous Authentication based on Hand Micro-movement during Smartphone Form Filling by Seated Human Subjects. DOI: 10.5220/0010225804240431 In Proceedings of the 7th International Conference on Information Systems Security and Privacy (ICISSP 2021), pages 424-431 ISBN: 978-989-758-491-6 Copyrightc 2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved still, once compromised, fail to protect the user’s account and data. In contrast, continuous authentication, based on traits of human behavior, can offer additional security measures in the device to authenticate against unauthorized users, even after the entry-point and one-time authentication has been compromised. To this end, we have collected a new data-set of multiple behavioral biometric modalities (49 users) when a user fills out an account recovery form in sitting using an Android app. These include motion events (acceleration and angular velocity), touch and swipe events, keystrokes, and pattern tracing. In this paper, we focus on authentication based on motion events by evaluating a set of score level fusion techniques to authenticate users based on the acceleration and angular velocity data. The best EERs of 2.4% and 6.9% for intra- and inter-session respectively, are achieved by fusing acceleration and angular velocity using Nandakumar et al.’s likelihood ratio (LR) based score fusion.« less
  5. Among the different types of skin cancer, melanoma is considered to be the deadliest and is difficult to treat at advanced stages. Detection of melanoma at earlier stages can lead to reduced mortality rates. Desktop-based computer-aided systems have been developed to assist dermatologists with early diagnosis. However, there is significant interest in developing portable, at-home melanoma diagnostic systems which can assess the risk of cancerous skin lesions. Here, we present a smartphone application that combines image capture capabilities with preprocessing and segmentation to extract the Asymmetry, Border irregularity, Color variegation, and Diameter (ABCD) features of a skin lesion. Using themore »feature sets, classification of malignancy is achieved through support vector machine classifiers. By using adaptive algorithms in the individual data-processing stages, our approach is made computationally light, user friendly, and reliable in discriminating melanoma cases from benign ones. Images of skin lesions are either captured with the smartphone camera or imported from public datasets. The entire process from image capture to classification runs on an Android smartphone equipped with a detachable 10x lens, and processes an image in less than a second. The overall performance metrics are evaluated on a public database of 200 images with Synthetic Minority Over-sampling Technique (SMOTE) (80% sensitivity, 90% specificity, 88% accuracy, and 0.85 area under curve (AUC)) and without SMOTE (55% sensitivity, 95% specificity, 90% accuracy, and 0.75 AUC). The evaluated performance metrics and computation times are comparable or better than previous methods. This all-inclusive smartphone application is designed to be easy-to-download and easy-to-navigate for the end user, which is imperative for the eventual democratization of such medical diagnostic systems.« less