skip to main content


Title: AEROKEY: Using Ambient Electromagnetic Radiation for Secure and Usable Wireless Device Authentication
Wireless connectivity is becoming common in increasingly diverse personal devices, enabling various interoperation- and Internet-based applications and services. More and more interconnected devices are simultaneously operated by a single user with short-lived connections, making usable device authentication methods imperative to ensure both high security and seamless user experience. Unfortunately, current authentication methods that heavily require human involvement, in addition to form factor and mobility constraints, make this balance hard to achieve, often forcing users to choose between security and convenience. In this work, we present a novel over-the-air device authentication scheme named AEROKEY that achieves both high security and high usability. With virtually no hardware overhead, AEROKEY leverages ubiquitously observable ambient electromagnetic radiation to autonomously generate spatiotemporally unique secret that can be derived only by devices that are closely located to each other. Devices can make use of this unique secret to form the basis of a symmetric key, making the authentication procedure more practical, secure and usable with no active human involvement. We propose and implement essential techniques to overcome challenges in realizing AEROKEY on low-cost microcontroller units, such as poor time synchronization, lack of precision analog front-end, and inconsistent sampling rates. Our real-world experiments demonstrate reliable authentication as well as its robustness against various realistic adversaries with low equal-error rates of 3.4% or less and usable authentication time of as low as 24 s.  more » « less
Award ID(s):
1845469 2107020
NSF-PAR ID:
10326397
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Volume:
6
Issue:
1
ISSN:
2474-9567
Page Range / eLocation ID:
1 to 29
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. 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 even 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. 
    more » « less
  2. Passive RFID technology is widely used in user authentication and access control. We propose RF-Rhythm, a secure and usable two-factor RFID authentication system with strong resilience to lost/stolen/cloned RFID cards. In RF-Rhythm, each legitimate user performs a sequence of taps on his/her RFID card according to a self-chosen secret melody. Such rhythmic taps can induce phase changes in the backscattered signals, which the RFID reader can detect to recover the user’s tapping rhythm. In addition to verifying the RFID card’s identification information as usual, the backend server compares the extracted tapping rhythm with what it acquires in the user enrollment phase. The user passes authentication checks if and only if both verifications succeed. We also propose a novel phase-hopping protocol in which the RFID reader emits Continuous Wave (CW) with random phases for extracting the user’s secret tapping rhythm. Our protocol can prevent a capable adversary from extracting and then replaying a legitimate tapping rhythm from sniffed RFID signals. Comprehensive user experiments confirm the high security and usability of RF-Rhythm with false-positive and false-negative rates close to zero. 
    more » « less
  3. null (Ed.)
    User authentication is a critical process in both corporate and home environments due to the ever-growing security and privacy concerns. With the advancement of smart cities and home environments, the concept of user authentication is evolved with a broader implication by not only preventing unauthorized users from accessing confidential information but also providing the opportunities for customized services corresponding to a specific user. Traditional approaches of user authentication either require specialized device installation or inconvenient wearable sensor attachment. This article supports the extended concept of user authentication with a device-free approach by leveraging the prevalent WiFi signals made available by IoT devices, such as smart refrigerator, smart TV, and smart thermostat, and so on. The proposed system utilizes the WiFi signals to capture unique human physiological and behavioral characteristics inherited from their daily activities, including both walking and stationary ones. Particularly, we extract representative features from channel state information (CSI) measurements of WiFi signals, and develop a deep-learning-based user authentication scheme to accurately identify each individual user. To mitigate the signal distortion caused by surrounding people’s movements, our deep learning model exploits a CNN-based architecture that constructively combines features from multiple receiving antennas and derives more reliable feature abstractions. Furthermore, a transfer-learning-based mechanism is developed to reduce the training cost for new users and environments. Extensive experiments in various indoor environments are conducted to demonstrate the effectiveness of the proposed authentication system. In particular, our system can achieve over 94% authentication accuracy with 11 subjects through different activities. 
    more » « less
  4. 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 that 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. 
    more » « less
  5. Abstract

    Biometric locking systems offer a seamless integration of an individual's physiological characteristics with secure authentication. However, they suffer from limitations such as false positive and negative authentication, environmental interference, and varying disadvantages across multiple authentication methods. To address these limitations, this study develops a soft smart biopatch for a continuous cardiac biometric wearable device that can continuously gather novel biometric data from an individual's heart sound for authentication with minimal error (less than 0.5%). The device is designed to be discreet and user‐friendly, and it employs soft biocompatible materials to ensure comfort and ease of use. The patch system incorporates a miniaturized microphone to monitor sounds over long periods and multiple dimensions, enhancing the reliability of the biometric data. Furthermore, the use of machine‐learning algorithms has enabled the creation of unique identification keys for individuals based on the continuous monitoring properties of the low‐cost device. These advantages make it more effective and efficient than traditional biometric systems, with the potential to enhance the security of mobile devices and door locks.

     
    more » « less