Verificación de identidad en la educación virtual mediante análisis biométrico basado en la dinámica del tecleo

Virtual education has become one of the tools most widely used by students at all educational levels, not just because of its convenience and flexibility, but also because it can expand educational coverage. All these benefits also bring along multiple issues in terms of security and reliability in the evaluation the of student’s knowledge because traditional identity verification strategies, such as the combination of username and password, do not guarantee that the student enrolled in the course really takes the exam. Therefore, a system with a different type of verification strategy should be designed to differentiate valid users from impostors. This study proposes a new verification system based on distances computed among Gaussian Mixture Models created with different writing task. The proposed approach is evaluated in two different modalities namely intrusive verification and non-intrusive verification. The intrusive mode provides a false positive rate of around 16 %, while the non-intrusive mode provides a false positive rate of 12 % In addition, the proposed strategy for non-intrusive verification is compared to a work previously reported in the literature and the results show that our approach reduces the equal error rate in about 24.3 %. The implemented strategy does not need additional hardware; only the computer keyboard is required to complete the user verification, which makes the system attractive, flexible, and practical for virtual education platforms.La educación virtual se ha convertido en una de las herramientas más utilizadas por los estudiantes en todos los niveles educativos, no solo por la comodidad y la flexibilidad, sino también por la posibilidad de ampliar la cobertura educativa en una población. Todos estos beneficios traen consigo múltiples problemas de seguridad y confiabilidad a la hora de evaluar el proceso de aprendizaje del estudiante, ya que las estrategias tradicionales de verificación de identidad, como la combinación de nombre de usuario y contraseña, no garantizan que el estudiante matriculado en el curso realmente realice el examen. Por lo tanto, es necesario diseñar un sistema con otro tipo de estrategia de verificación para diferenciar un usuario válido de un impostor. Este estudio propone un nuevo método de verificación, basado en el cálculo de distancias entre los modelos de mezclas gaussianas creados con diferentes tareas de escritura. El enfoque propuesto es evaluado en dos modalidades diferentes llamadas verificación intrusiva y verificación no intrusiva. El modo intrusivo proporciona una tasa de falsos positivos de 16 %, mientras el modo no intrusivo provee una tasa de falsos positivos de 12 %. Además, la estrategia propuesta para verificación no intrusiva es comparada con un trabajo previamente reportado en la literatura y los resultados muestran que nuestro enfoque reduce la tasa de error en aproximadamente un 24.3 %. La estrategia implementada no necesita hardware adicional, solo es requerido el teclado del computador para realizar la verificación, lo que hace que el sistema sea atractivo y flexible para ser usado en plataformas de educación virtual

Identification of User Behavioural Biometrics for Authentication using Keystroke Dynamics and Machine Learning

This thesis focuses on the effective classification of the behavior of users accessing computing devices to authenticate them. The authentication is based on keystroke dynamics, which captures the users behavioral biometric and applies machine learning concepts to classify them. The users type a strong passcode ”.tie5Roanl” to record their typing pattern. In order to confirm identity, anonymous data from 94 users were collected to carry out the research. Given the raw data, features were extracted from the attributes based on the button pressed and action timestamp events. The support vector machine classifier uses multi-class classification with one vs. one decision shape function to classify different users. To reduce the classification error, it is essential to identify the important features from the raw data. In an effort to confront the generation of features from attributes an efficient feature extraction algorithm has been developed, obtaining high classification performance are now being sought. To handle the multi-class problem, the random forest classifier is used to identify the users effectively. In addition, mRMR feature selection has been applied to increase the classification performance metrics and to confirm the identity of the users based on the way they access computing devices. From the results, we conclude that device information and touch pressure effectively contribute to identifying each user. Out of them, features that contain device information are responsible for increasing the performance metrics of the system by adding a token-based authentication layer. Based upon the results, random forest yields better classification results for this dataset. The research will contribute significantly to the field of cyber-security by forming a robust authentication system using machine learning algorithms

Система розпізнавання особи за поведінковими особливостями

В бакалаврському дипломному проекті реалізовано алгоритм розпізнавання особи за її поведінковими особливостями, що призначений для аутентифікування людини та надання їй доступу до системи при успішному розпізнаванні особистості (на прикладі форми логіну). В програму інтегровано систему машинного навчання (Brain.js), що дозволяє за отриманими через дії особи даними визначити, чи є вона користувачем або зловмисником. Програмний продукт створений за допомогою мови JavaScript, а саме фреймворка React Native, а також Android Studio, SDK та емулятора мобільного пристрою. Для отримання даних користувача, за якими надалі будуть проводитись розрахунки, використовується фізичний мобільний пристрій Samsung S10 на базі Android 9.In this Bachelor’s Degree project, an algorithm for identifying an individual for behavioral features has been implemented, as well as signs for authenticating people and providing access to the system in case of successful detection of personality. The program integrates a machine learning system (Brain.js), which allows the data obtained through the actions of a person to determine whether they are users or an attacker. The software product is created using the JavaScript language, the React Native framework, as well as Android Studio, SDK and mobile device emulator. To obtain user’s data, the Samsung S10 physical mobile device based on Android 9 is used. The data will be used for further calculations

Abstraction Fashion: Seeing and Making Network Abstractions and Computational Fashions

Human life today is enmeshed with network organisms. What we value, the ways we talk, and the subject matter we pay attention to are all dependent on and depended upon by the networks that dominate our imagination. The internet, private social platforms, and the virtual and physical supply chains that create the hardware, software, and memetic abstractions with which we think are all examples of network organisms. Each has found a viability mechanism that permits it to survive and thrive in the present moment. Each viability mechanism creates its own unique incentives for self-perpetuation, which drive the outward appearances with which we are familiar. These incentives manifest as product forms, interface abstractions, and socially optimized beliefs and identities. To grapple with what drives the abstractions these network organisms output, this dissertation builds a worldview for seeing and making with computational networks. Computing machines are composed of abstractions, simulate abstractions, and project their abstractions onto the world. Creating in this medium requires resources that can be acquired through attention manipulation and fashion performance. The text culminates in an appendix documenting ewaste club, an art research-creation project that combines wearable cameras, supply chain inspired fashion, and disposable computers. Through a mixture of practical projects, historical analysis, and technical explanation, this dissertation proposes several new concepts linking fashion, the arts, and computation to making in the time of networks