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 authentication 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%.
more »
« less
MicPrint: acoustic sensor fingerprinting for spoof-resistant mobile device authentication
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
- NSF-PAR ID:
- 10156908
- Date Published:
- Journal Name:
- Proceedings of the 16th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services
- Page Range / eLocation ID:
- 248 - 257
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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 infeasible 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%.more » « less
-
Smart mobile devices have become an integral part of people's life and users often input sensitive information on these devices. However, various side channel attacks against mobile devices pose a plethora of serious threats against user security and privacy. To mitigate these attacks, we present a novel secure Back-of-Device (BoD) input system, SecTap, for mobile devices. To use SecTap, a user tilts her mobile device to move a cursor on the keyboard and tap the back of the device to secretly input data. We design a tap detection method by processing the stream of accelerometer readings to identify the user's taps in real time. The orientation sensor of the mobile device is used to control the direction and the speed of cursor movement. We also propose an obfuscation technique to randomly and effectively accelerate the cursor movement. This technique not only preserves the input performance but also keeps the adversary from inferring the tapped keys. Extensive empirical experiments were conducted on different smart phones to demonstrate the usability and security on both Android and iOS platforms.more » « less
-
more » « less
Abstract Rapid advances in the Internet‐of‐Things (IoT) domain have led to the development of several useful and interesting devices that have enhanced the quality of home living and industrial automation. The vulnerabilities in the IoT devices have rendered them susceptible to compromise and forgery. The problem of device authentication, that is, the question of whether a device's identity is what it claims to be, is still an open problem. Device fingerprinting seems to be a promising authentication mechanism. Device fingerprinting profiles a device based on information available about the device and generate a robust, verifiable and unique identity for the device. Existing approaches for device fingerprinting may not be feasible or cost‐effective for the IoT domain due to the resource constraints and heterogeneity of the IoT devices. Due to resource and cost constraints, behavioral fingerprinting provides promising directions for fingerprinting IoT devices. Behavioral fingerprinting allows security researchers to understand the behavioral profile of a device and to establish some guidelines regarding the device operations. In this article, we discuss existing approaches for behavioral fingerprinting of devices in general and evaluate their applicability for IoT devices. Furthermore, we discuss potential approaches for fingerprinting IoT devices and give an overview of some of the preliminary attempts to fingerprint IoT devices. We conclude by highlighting the future research directions for fingerprinting in the IoT domain.
This article is categorized under:
Application Areas > Science and Technology
Application Areas > Internet
Technologies > Machine Learning
Application Areas > Industry Specific Applications
-
Universal Serial Bus (USB) ports are a ubiquitous feature in computer systems and offer a cheap and efficient way to provide power and data connectivity between a host and peripheral devices. Even with the rise of cloud and off-site computing, USB has played a major role in enabling data transfer between devices. Its usage is especially prevalent in high-security environments where systems are ‘air-gapped’ and not connected to the Internet. However, recent research has demonstrated that USB is not nearly as secure as once thought, with different attacks showing that modified firmware on USB mass storage devices can compromise a host system. While many defenses have been proposed, they require user interaction, advanced hardware support (incompatible with legacy devices), or utilize device identifiers that can be subverted by an attacker. In this paper, we present Time-Print, a novel timing-based fingerprinting method, for identifying USB mass storage devices. We create a fingerprint by timing a series of read operations from different locations on a drive, as the timing variations are unique enough to identify individual USB devices. Time-Print is low overhead, completely software-based, and does not require any extra or specialized hardware. To validate the efficacy of Time-Print, we examine more than 40 USB flash drives and conduct experiments in multiple authentication scenarios. The experimental results show that Time-Print can (1) identify known/unknown brand/model USB devices with greater than 99.5% accuracy, (2) identify seen/unseen devices of the same brand/model with 95% accuracy, and (3) classify USB devices from the same brand/model with an average accuracy of 98.7%.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3360774.3360801
