Device Fingerprinting Research Articles

Microbial Sampling and Analysis Taken from Fingerprint System Devices Located at Al-Yarmouk Teaching Hospital in Baghdad - Iraq

Biometric devices (BDs) are nowadays common in use for a variety of purposes. This system involves physical contact between the skin and surface of the device, which is likely to be contaminated by microorganisms of multiple users. However, it can be a source of multiple microbiological contaminations. This study was done at AL-Yarmouk Teaching Hospital-Baghdad- Iraq, during the period started from 15th November 2023 till 15th March 2024. It was designed to find out the most common bacteria and fungi that might contaminate the fingerprint devices and performing antimicrobial susceptibility tests. Forty-six sterile swab samples taken from Twenty-three fingerprint system devices distributed in twelve different hospital departments. From each device we took two swabs, one before sterilization other after, and tested sensitivity patterns against 16 antimicrobials. Four Gram positive bacteria scored on fingerprint devices, in Medicine, Surgery, Orthopedics and Obstetrics & Gynecology departments represented by Coagulase-negative Staphylococci (CoNS). Response to sensitivity test for antibiotics, especially Moxifloxacin, Linezolid, Teicoplanin, Vancomycin, Tigecycline and Rifampicin, 100%, as well other antibiotics but in varying degrees, and not any type of fungi were isolated. In conclusion, Gram positive bacteria population was the most common organisms, and highly response in varying degrees to the most Sixteen antibiotics used in sensitivity test, however, should proper maintenance and educational practices must be applied to minimize contamination.

Optimizing Touchless Fingerprint Identification: A Machine Learning Approach to Modelling and Performance Evaluation

This paper explored the modelling and performance analysis of a smartphone-based fingerprint identification system using Convolutional Neural Networks (CNN). The research developed a theoretical framework to validate picture-based fingerprint identification as a feasible alternative to traditional touch-based methods. A modified Automated Fingerprint Identification System (AFIS) model served as the study's foundation. To enhance the model's capabilities, data from two databases, IIT India and SOCOFing, were utilized. The evaluation of the CNN architecture focused on mobile device fingerprint recognition. It emphasized key processes such as data pre-processing, model training, and the optimization of the CNN through a Siamese-CNN approach to boost accuracy and efficiency. Python scripts developed for this purpose were converted to Android code using TensorFlow for deployment on Android devices. Performance metrics, including identification accuracy, processing speed, and resource utilization, were analysed to determine the system's feasibility. The results demonstrated that CNN-based fingerprint identification systems hold significant promise for delivering robust and reliable biometric authentication on smartphones, highlighting both their practical applications and limitations. decrease medical as well as financial burden, hence improving the management of cirrhotic patients. These predictors, however, need further work to validate reliability.

Analysis of authentication methods for full-stack applications and implementation of a web application with an integrated authentication system

The subject of research is methods and techniques for secure user authentication in web applications. The goal of the work is to analyse authentication methods and implement a web application with an authentication system integrating JWT tokens and the OAuth v2.0 standard. The article solves the tasks of analysis of the main protocols and methods of user authentication in web applications, implementation of authentication based on the OAuth 2.0 standard and JWT access/refresh token, and analysis of the risks of vulnerabilities and attacks for the implemented web applications. Methods used: comparison, empirical analysis, calculation methods. The next results have been obtained: analysed the protocols and methods of user authentication in web applications; selected authentication methods of JWT token and OAuth v2.0 standard for building modern web applications; created a web application based on the selected authentication methods in web applications; analysed the risks of vulnerabilities and attacks in web applications. Conclusions: The most well-known authentication methods for web applications are analyzed. It is established that most modern authentication methods have many disadvantages, which leads to increased risks when using these authentication methods. It is shown that one of the most reliable methods of web application user data security is the use of a combination of JWT Access/Refresh token and browser fingerprints. The implementation, configuration, and analysis of this methodology have shown that this combination provides the most reliable prevention of token theft and use from another computer. OAuth 2.0 authentication was also implemented. The study found that delegating authentication to services such as Facebook or Google can provide a low risk of attacks and vulnerabilities for a web application. It is noted that authentication using OAuth 2.0 can be compromised only at the beginning of the connection between the client and the server, or rather when the client first sends initial information from the browser fingerprints. This information is sent over the secure HTTPS (Hypertext Transfer Protocol Secure) protocol, so the risk of compromising OAuth 2.0 authentication is low.

DeepFPD: Browser Fingerprinting Detection via Deep Learning With Multimodal Learning and Attention

DeepFPD: Browser Fingerprinting Detection via Deep Learning With Multimodal Learning and Attention

A channel resilient RFF extraction scheme for cyclic prefix contained systems

Radio frequency fingerprint (RFF) based identification technique has been proved efficient for ensuring the validity of devices connected to network. However, it is still a tough task to extract robust RFF in the scenarios with multi-path channel and moving terminals. To solve this problem, this paper proposes a channel-resilient RFF extraction scheme which can effectively reduce the influences from complex channel condition and retain robust device fingerprint. In the proposed system, blind synchronization and symbol-scale carrier frequency offset (CFO) estimation are designed for signal preprocessing for preparations of the following RFF extraction. A cyclic-prefix based de-channel algorithm (CPDCA) which can effectively weaken channel interference is proposed to meet the channel robustness of our system. Additionally, symbol-scale feature stacking algorithm (SFSA) is applied for RFF denoising, which can further enhance the performance of proposed system. Experiments using practical dataset collected from Long Term Evolution (LTE)-V2X communication system has been carried out under different signal-to-noise ratio (SNR). The results demonstrate that the proposed scheme has the ability to extract channel-robust RFF and to achieve reliable classification performance under complex channel conditions.

Pattern of Bacterial Flora and Effect of Disinfection on Fingerprinting Biometric Devices at a Tertiary Care Health Facility: An Interventional Study.

Introduction Recently, many public and private sector institutions and hospitals have installed biometric fingerprint devices for attendance purposes. This step is taking us toward modernization but biometric devices have their cons and pros; if not sterilized at regular intervals, then it may be a potent cause of transmission of various infections. Many studies have reported the presence of coagulase-negative Staphylococcus aureus (CONS), methicillin-resistant Staphylococcus aureus (MRSA), Escherichiacoli, Pseudomonas, and others. Aim To study the pattern of bacterial flora and the effect of disinfection on fingerprinting biometric devices at a tertiary care health facility. Materials andmethods A total of 138 biometric devices were used, out of which 105 were frequently (at least 50 uses per day) used and functional. So, 105 samples were collected on day zero (baseline), of which 43 and 62 were from clinical and non-clinical groups, respectively. The devices were disinfected with isopropyl alcohol (w/v 70%) and subsequent samples were taken on day 1 (after 24 hours) and day 7. The samples were collected and transported to the microbiology lab for culture and incubation. Statistical analysis was performed usingSPSS software version 25 (IBM Corp., Armonk, NY) employing chi-square, Cochran's Q test, and post hoc test. A p-value ≤0.05 at a 95% confidence interval was considered to be statistically significant. Results At baseline (day 0), bacterial growth was observed in 13 (38%) devices from the clinical group and 10 (20%) from the non-clinical group. After disinfection with 70% isopropyl alcohol, bacterial growth was reduced by 83% on day 1 but increased by 82% on day 7. These changes were statistically significant (p ≤ 0.05). Conclusion The present study concluded the definite presence of bacterial flora on the biometric fingerprint devices which are prone to carry and transmit microorganisms indirectly from person to person. The surface of biometric fingerprinting devices should be disinfected daily. If not possible, it should be done on an average of every third day to control and minimize the transmission of microorganisms.

A Browser Fingerprint Authentication Scheme Based on the Browser Cache Side-Channel Technology

A Browser Fingerprint Authentication Scheme Based on the Browser Cache Side-Channel Technology

FP-tracer: Fine-grained Browser Fingerprinting Detection via Taint-tracking and Entropy-based Thresholds

Browser fingerprinting is an effective technique to track web users by building a fingerprint from their browser attributes. It is also stealthy because the tracker uses legitimate JavaScript API calls offered by the browser engine, which can be obfuscated before they are sent to a (third-party) server. Current browser fingerprinting methodologies employ coarse-grained collection and classification techniques, such as binary classification of fingerprinters based on the number of non-obfuscated exfiltrated attributes. As a result, they produce inconsistent findings. Meanwhile, the privacy of millions of web users is at risk daily. We address this gap by presenting FP-tracer, a novel methodology to detect and classify browser fingerprinters based on dynamic taint tracking and joint entropy classification. Our methodology enables detecting first- and third-party fingerprinters even when they use obfuscation by tainting attributes, propagating them, and logging when they are leaked (via 62 sources and 25 sinks). Moreover, it discriminates the invasiveness of fingerprinting activities, even from the same service, by measuring the joint entropy of the collected attributes and clustering them. We implement FP-tracer by extending Foxhound, a privacy-oriented Firefox fork with numeric type tainting, more taint tracking sources and sinks, support for multiple sources, and better logging capabilities. We embed our implementation in our automated crawling infrastructure, which is capable of testing websites in parallel using programmable and reproducible logic. We will open-source our implementation. We evaluate FP-tracer by performing a large-scale crawl over the Tranco Top 100K, and detect, amongst others, audio, canvas, and storage fingerprinting on the web. Among others, we find high fingerprinting activities in 8% of domains, with more moderate activity reaching 75%. Notably, fingerprinting is almost five times more likely to be performed by third-party scripts for high activity levels. In addition, we measure that the most severe category of fingerprinting obfuscates 46% of transmitted attributes, and 38% of fingerprinters involve two or more domains. Finally, we find that existing consent banners do not provide an effective defense against browser fingerprinting

Research on machine learning-based device fingerprint recognition technology for power internet of things

Research on machine learning-based device fingerprint recognition technology for power internet of things

An IOT Based Vehicle Theft Detection and Remote Engine Locking System

Abstract: An IoT based vehicle theft detection and remote engine locking system is GSM (Global System for Mobile Communication) technology that helps the users to identify the vehicle in the theft mode and allow you to use the controlling mechanism technique. These days, there are a ton of cars being stolen. It's a big problem because crime rates are going up, and it's making it harder to keep vehicles safe. Criminals are becoming smarter and have reached the stage of applications present against the vehicles safety system. Vehicle theft has become a considerable issue which should be traced and prevented. The new system solves many problems and is affordable. Also the proposed system reducing complications by making use of some high priced products like ignition key, microcontroller. In the proposed method, we include an additional feature where controlling mechanisms remotely lock the vehicle engine and prevent theft. In proposed technique, user can start/stop the vehicle either by using the ignition key, which tracks exact location of the vehicle using the application. This paper talks about a great anti-theft system for vehicles. It uses Global Positioning System(GPS),Global System for Mobile Communication(GSM),and Biometrics technologies(i.e fingerprint ) to find the vehicle and confirm the owner's identity. Fingerprint device captures the fingerprints and matches the distinctiveness of every print scan by the device and compares it to the one keeps in its module or native system database.

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

Bluetooth and WiFi Dataset for Real World RF Fingerprinting of Commercial Devices

CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

The project "Credit Card Fraud Detection with FBA" aims to develop a robust and efficient system for identifying and preventing fraudulent activities associated with credit card transactions within the context of Fulfilment by Amazon (FBA). With the increasing prevalence of online transactions, credit card fraud has become a significant concern for both consumers and businesses. FBA, being a prominent e-commerce platform, requires a sophisticated fraud detection mechanism to ensure secure and trustworthy transactions. The project utilizes advanced machine learning algorithms to analyse transaction data and detect patterns indicative of potential fraud. These algorithms are trained on historical data, enhancing their ability to recognize anomalies in real-time transactions. The system incorporates a multi- layered approach, utilizing address verification, device fingerprinting, and IP geolocation to validate the legitimacy of transactions and minimize false positives. Real-time monitoring is a key component of the system, allowing for immediate detection and response to suspicious activities. Manual review processes are integrated, involving trained personnel to investigate flagged transactions and make informed decisions based on additional information or customer verification. This human element adds an extra layer of scrutiny, contributing to the reliability of the fraud detection system. The project aims to create a dynamic and adaptive fraud detection system that evolves wit emerging fraud tactics. Regular updates and improvements will be implemented to address new challenges and enhance the overall security of credit card transactions within the FBA ecosystem. The successful implementation of this project will contribute to a safer and more secure online shopping experience for FBA users, fostering trust and confidence in the platform. Index terms: Credit card fraud detection, Fulfilment by Amazon (FBA), Machine learning algorithms, Fraudulent activities, Online transactions, Real-time monitoring, Multi-layered approach, Address verification, Device fingerprinting, IP geolocation, Manual review processes, Dynamic and adaptive system, Emerging fraud tactics, Security enhancements, Online shopping experience, Trust and confidence building

Res-DFNN: An NN-Based Device Fingerprint Extraction Method Using Network Packet Data

The past few years have witnessed a wider adoption of Internet of Things (IoT) devices. Since IoT devices are usually deployed in an open and uncertain environment, device authentication is of great importance. However, traditional device fingerprint (DF) extraction methods have several disadvantages. First, existing DF extraction methods need private information from devices to compute DFs, which puts the privacy of devices at stake. Second, the manually designing features-based methods suffer from poor performance. To tackle these limitations, we propose a Linear Residual Neural Network-based DF extraction method, Res-DFNN, which utilizes network packet data in the pcap file to generate DF. The key block is designed according to symmetry, and it is verified by simulation that our method achieves better performance in both non-private and privacy-preserving scenarios.

CADFA: A Clock Skew-Based Active Device Fingerprint Authentication Scheme for Class-1 IoT Devices

CADFA: A Clock Skew-Based Active Device Fingerprint Authentication Scheme for Class-1 IoT Devices

Studying the Robustness of Anti-Adversarial Federated Learning Models Detecting Cyberattacks in IoT Spectrum Sensors

Device fingerprinting combined with Machine and Deep Learning (ML/DL) report promising performance when detecting cyberattacks targeting data managed by resource-constrained spectrum sensors. However, the amount of data needed to train models and the privacy concerns of such scenarios limit the applicability of centralized ML/DL-based approaches. Federated learning (FL) addresses these limitations by creating federated and privacy-preserving models. However, FL is vulnerable to malicious participants, and the impact of adversarial attacks on federated models detecting spectrum sensing data falsification (SSDF) attacks on spectrum sensors has not been studied. To address this challenge, the first contribution of this work is the creation of a novel dataset suitable for FL and modeling the behavior (usage of CPU, memory, or file system, among others) of resource-constrained spectrum sensors affected by different SSDF attacks. The second contribution is a pool of experiments analyzing and comparing the robustness of federated models according to i) three families of spectrum sensors, ii) eight SSDF attacks, iii) four scenarios dealing with unsupervised (anomaly detection) and supervised (binary classification) federated models, iv) up to 33% of malicious participants implementing data and model poisoning attacks, and v) four aggregation functions acting as anti-adversarial mechanisms to increase the models robustness.

Lightweight Strong PUF for Resource-Constrained Devices

Physical Unclonable Functions are security primitives that exploit the variation in integrated circuits’ manufacturing process, and, as a result, each instance processes applied stimuli differently. This feature can be used to provide a unique fingerprint of the electronic device, or as an interesting alternative to classic key storage methods. Due to their nature, they are often considered an element of the Internet of Things nodes. However, their application heavily depends on resource consumption. Lightweight architectures are proposed in the literature but are technology-dependent or still introduce significant hardware overhead. This paper presents a lightweight, Strong PUF based on ring oscillator architecture, which offers small hardware overhead and sufficient security levels for resource-constrained Internet of Things devices. The PUF design utilizes a Linear Feedback Shift Register-based scramble module to generate many challenge–response pairs from a small number of ring oscillators and a control module to manage the response generation process. The proposed PUF can be used as a Weak PUF for key generation or a Strong PUF for device authentication.

Detect Insider Attacks in Industrial Cyber-physical Systems Using Multi-physical Features-based Fingerprinting

ICPS software and hardware suffer from low update frequency, making it easier for insiders to bypass external defenses and launch concealed destructive attacks. To address these concerns, we design a device fingerprinting method based on multi-physical features, augmenting current intrusion detection techniques in the ICPS environment. In this article, we use the sorting system as an example, demonstrating that the proposed device fingerprinting technology has generality in the intrusion detection of ICPS control flow. Specifically, we first formalize the physical model of the sorting system to analyze the critical device features. Then, we extract these physical features from the sensor data collected in a physical testbed. Utilizing featurized data, we train a classifier that generates fingerprints in real-time in the production environment. Moreover, we develop a differential detection model based on device fingerprints to discover stealthy insider attacks efficiently. We evaluate the proposed method in a real-world testbed. Experiment results show that the detecting performance of classifiers approaches 100% when the the number of component types is small.

MotoPrint: Reconfigurable Vibration Motor Fingerprint via Homologous Signals Learning

Device fingerprints can satisfy the high-security requirement of modern mobile applications (e.g., mobile payments) by guaranteeing the operation is performed on a trusted device. However, existing works on device fingerprints are weak to leakage, which leads to an irreversible failure of the device fingerprint authentication system after suffering from fingerprint theft attacks. The vulnerability drives us to propose a reconfigurable device fingerprint, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , that can recover the system after suffering from such attacks. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> stems from the motor vibration that can represent in both signals of the accelerometer and the gyroscope (i.e., they are homologous motion signals). Therefore, we designed a two-path feature extracting network and a sensor-independent training strategy to eliminate sensor noise that can decline authentication performance. In addition, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> has a complete reconfiguration mechanism to cope with fingerprint leakage, which brings the damaged authentication system back to health. The evaluation of 80 stand-alone vibration motors and 20 in-built ones shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> can achieve high authentication accuracy of 98.5%. Meanwhile, we also demonstrate the reconfigured <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> , which can also effectively indicate the device's uniqueness with over 98% accuracy, is independent of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MotoPrint</i> s under other stimulating codes.

Balancing security and user experience in the evolving digital landscape

In today's digital landscape, the prevalence of automated threats poses a significant challenge to online security. This study addresses the evolving landscape of online security by investigating next-generation CAPTCHAs, which aim to strike a balance between heightened security and an enhanced user experience. The relentless arms race between automated threats and online security necessitates the development of innovative solutions capable of countering advanced technological threats while ensuring a seamless user experience. The primary objective of this research is to explore and evaluate the effectiveness the presented approach in enhancing online security and user satisfaction. We examine how the integration of behavioral biometrics, gamification techniques, and supplementary tools such as device fingerprinting, geolocation, and browser attributes can contribute to a more robust and user-friendly CAPTCHA experience. Our study employs a comprehensive methodology, including a thorough literature review, and data collection from diverse sources. We evaluate the authenticity of these advanced systems, taking into account their ability to adjust to ever-changing digital environments. However, challenges persist in striking the right balance between security and convenience, addressing privacy concerns, and adapting to evolving digital landscapes. These findings validate the critical importance of ongoing research and innovation technology to safeguard online platforms effectively.

Image Technology Investigation Based on Fingerprint Devices and Artificial Intelligence

Image Technology Investigation Based on Fingerprint Devices and Artificial Intelligence