Snap Forensics: A Tradeoff between Ephemeral Intelligence and Persistent Evidence Collection

Digital evidence needs to be made persistent so that it can be used later. For citizen forensics, sometimes intelligence cannot or should not be made persistent forever. In this position paper, we propose a form of snap forensics by defining an elastic duration of evidence/intelligence validity. Explicitly declaring such a duration could unify the treatment of both ephemeral intelligence and persistent evidence towards more flexible storage to satisfy privacy requirements

A Brief Look into Biometrics and One Use in Higher Education

Biometrics for the purpose of identification is not a new concept, nor is it limited to one specific field. Both physical and biological unique characteristics are being utilized today by biometric technology as a means of recognition (Krishan & Mostafavi, 2018). How exactly are biometrics used today in authorization and identification systems? What are some of the advantages of using biometric technologies over traditional methods of authentication? What are some of the security and privacy concerns of using biometric technology? In this paper, by reviewing multiple published articles in the field of biometrics, we seek to answer these questions, provide insight into the future of biometrics, and discuss the varying responses that biometrics has received from end users, including biometric legislation. We will then look deeper into one particular area of biometric technology, voice recognition, by proposing research in higher education to be conducted on this subject

Human identification using compressed ECG signals

As a result of the increased demand for improved life styles and the increment of senior citizens over the age of 65, new home care services are demanded. Simultaneously, the medical sector is increasingly becoming the new target of cybercriminals due the potential value of users' medical information. The use of biometrics seems an effective tool as a deterrent for many of such attacks. In this paper, we propose the use of electrocardiograms (ECGs) for the identification of individuals. For instance, for a telecare service, a user could be authenticated using the information extracted from her ECG signal. The majority of ECG-based biometrics systems extract information (fiducial features) from the characteristics points of an ECG wave. In this article, we propose the use of non-fiducial features via the Hadamard Transform (HT). We show how the use of highly compressed signals (only 24 coefficients of HT) is enough to unequivocally identify individuals with a high performance (classification accuracy of 0.97 and with identification system errors in the order of 10(-2)).This work was supported by the MINECO grant TIN2013-46469-R (SPINY: Security and Privacy in the Internet of You) and the CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks)

Non-invasive multi-modal human identification system combining ECG, GSR, and airflow biosignals

A huge amount of data can be collected through a wide variety of sensor technologies. Data mining techniques are often useful for the analysis of gathered data. This paper studies the use of three wearable sensors that monitor the electrocardiogram, airflow, and galvanic skin response of a subject with the purpose of designing an efficient multi-modal human identification system. The proposed system, based on the rotation forest ensemble algorithm, offers a high accuracy (99.6 % true acceptance rate and just 0.1 % false positive rate). For its evaluation, the proposed system was testing against the characteristics commonly demanded in a biometric system, including universality, uniqueness, permanence, and acceptance. Finally, a proof-of-concept implementation of the system is demonstrated on a smartphone and its performance is evaluated in terms of processing speed and power consumption. The identification of a sample is extremely efficient, taking around 200 ms and consuming just a few millijoules. It is thus feasible to use the proposed system on a regular smartphone for user identification.This work was supported by MINECO grant TIN2013- 46469-R (SPINY: Security and Privacy in the Internet of You) and CAM grant S2013/ICE-3095 (CIBERDINE: Cybersecurity, Data, and Risks)

Continuous authentication based on data from smart devices

As technology moves forward to offer the user custom information, based on their activity, their habits, their hobbies… User authentication advances and provides them with ways to verify their identity with data that no one else possess, such as fingerprints, and it is becoming more popular. More devices are entering in the market, wearables, which provide an enhanced user experience by expanding the functionalities offered by a computer or a smartphone, and they are being incorporated to the user identification process. This thesis presents an approach to user authentication based on their movement, collecting data from an accelerometer placed on a smartwatch, to find out if it is a valid metric to distinguish among users when they are performing day to day activities. The recognition task is relied on artificial intelligence techniques, employing machine learning algorithms to generate a model that recognises a user and the activity that is being carried out, and a graphical user interface is provided so that users can try the system and incorporate new information. Using Waikato University developed software for machine learning algorithms, the system is developed using Python and Texas Instruments eZ430-Chronos smartwatch, and has been tested on a real environment, where several users were asked to perform different activities while wearing the watch.Según se mueve la tecnología hacia la personalización de la información basada en la actividad de los usuarios, sus hábitos y hobbies… La identificación de los usuarios avanza para proporcionar formas de verificación particulares para cada uno y que no posee nadie más, como es el caso del reconocimiento mediante la huella dactilar, y cada día gana más aceptación entre los usuarios. Los constantes lanzamientos de nuevos dispositivos weareables, que proporcionan una experiencia de usuario mejorada, ofrecen nuevas funcionalidades que extienden las que ya proporcionan los ordenadores o dispositivos móviles, y su uso se está incorporando a la verificación de usuarios. A lo largo de este trabajo se presenta un nuevo enfoque a la identificación de usuario basado en su movimiento, recolectando datos de un acelerómetro situado en un smartwatch, para averiguar si es una forma válida de diferenciar entre usuarios cuando están realizando actividades comunes del día a día. La tarea de identificación se confía a la inteligencia artificial, utilizando algoritmos de aprendizaje automático para generar modelos que sean capaces de reconocer a un usuario y la actividad que están realizando. Además, se proporciona una interfaz de usuario para que los usuarios puedan probar el sistema y ampliarlo con nuevos datos. Empleando el software de aprendizaje automático desarrollado por la Universidad de Waikato, el sistema está realizado en Python, usando el smartwatch Texas Instruments eZ460-Chronos, y ha sido probado en un entorno real donde se pidió a distintos usuarios que realizasen varias actividades mientras llevaban puesto el reloj.Ingeniería Informátic

ZEBRA: Zero-Effort Bilateral Recurring Authentication

Common authentication methods based on passwords, tokens, or fingerprints perform one-time authentication and rely on users to log out from the computer terminal when they leave. Users often do not log out, however, which is a security risk. The most common solution, inactivity timeouts, inevitably fail security (too long a timeout) or usability (too short a timeout) goals. One solution is to authenticate users continuously while they are using the terminal and automatically log them out when they leave. Several solutions are based on user proximity, but these are not sufficient: they only confirm whether the user is nearby but not whether the user is actually using the terminal. Proposed solutions based on behavioral biometric authentication (e.g., keystroke dynamics) may not be reliable, as a recent study suggests. \par To address this problem we propose ZEBRA. In ZEBRA, a user wears a bracelet (with a built-in accelerometer, gyroscope, and radio) on her dominant wrist. When the user interacts with a computer terminal, the bracelet records the wrist movement, processes it, and sends it to the terminal. The terminal compares the wrist movement with the inputs it receives from the user (via keyboard and mouse), and confirms the continued presence of the user only if they correlate. Because the bracelet is on the same hand that provides inputs to the terminal, the accelerometer and gyroscope data and input events received by the terminal should correlate because their source is the same – the user\u27s hand movement. In our experiments ZEBRA performed continuous authentication with 85% accuracy in verifying the correct user and identified all adversaries within 11 s. For a different threshold that trades security for usability, ZEBRA correctly verified 90% of users and identified all adversaries within 50 s

A Survey of Wearable Biometric Recognition Systems

The growing popularity of wearable devices is leading to new ways to interact with the environment, with other smart devices, and with other people. Wearables equipped with an array of sensors are able to capture the owner’s physiological and behavioural traits, thus are well suited for biometric authentication to control other devices or access digital services. However, wearable biometrics have substantial differences from traditional biometrics for computer systems, such as fingerprints, eye features, or voice. In this article, we discuss these differences and analyse how researchers are approaching the wearable biometrics field. We review and provide a categorization of wearable sensors useful for capturing biometric signals. We analyse the computational cost of the different signal processing techniques, an important practical factor in constrained devices such as wearables. Finally, we review and classify the most recent proposals in the field of wearable biometrics in terms of the structure of the biometric system proposed, their experimental setup, and their results. We also present a critique of experimental issues such as evaluation and feasibility aspects, and offer some final thoughts on research directions that need attention in future work