Improved Internet Security Protocols Using Cryptographic One-Way Hash Chains

In this dissertation, new approaches that utilize the one-way cryptographic hash functions in designing improved network security protocols are investigated. The proposed approaches are designed to be scalable and easy to implement in modern technology. The first contribution explores session cookies with emphasis on the threat of session hijacking attacks resulting from session cookie theft or sniffing. In the proposed scheme, these cookies are replaced by easily computed authentication credentials using Lamport\u27s well-known one-time passwords. The basic idea in this scheme revolves around utilizing sparse caching units, where authentication credentials pertaining to cookies are stored and fetched once needed, thereby, mitigating computational overhead generally associated with one-way hash constructions. The second and third proposed schemes rely on dividing the one-way hash construction into a hierarchical two-tier construction. Each tier component is responsible for some aspect of authentication generated by using two different hash functions. By utilizing different cryptographic hash functions arranged in two tiers, the hierarchical two-tier protocol (our second contribution) gives significant performance improvement over previously proposed solutions for securing Internet cookies. Through indexing authentication credentials by their position within the hash chain in a multi-dimensional chain, the third contribution achieves improved performance. In the fourth proposed scheme, an attempt is made to apply the one-way hash construction to achieve user and broadcast authentication in wireless sensor networks. Due to known energy and memory constraints, the one-way hash scheme is modified to mitigate computational overhead so it can be easily applied in this particular setting. The fifth scheme tries to reap the benefits of the sparse cache-supported scheme and the hierarchical scheme. The resulting hybrid approach achieves efficient performance at the lowest cost of caching possible. In the sixth proposal, an authentication scheme tailored for the multi-server single sign-on (SSO) environment is presented. The scheme utilizes the one-way hash construction in a Merkle Hash Tree and a hash calendar to avoid impersonation and session hijacking attacks. The scheme also explores the optimal configuration of the one-way hash chain in this particular environment. All the proposed protocols are validated by extensive experimental analyses. These analyses are obtained by running simulations depicting the many scenarios envisioned. Additionally, these simulations are supported by relevant analytical models derived by mathematical formulas taking into consideration the environment under investigation

USER INTERFACES FOR MOBILE DEVICES: TECHNIQUES AND CASE STUDIES

The interactive capabilities of portable devices that are nowadays increasingly available, enable mobile computing in diverse contexts. However, in order to fully exploit the potentialities of such technologies and to let end users benefit from them, effective and usable techniques are still needed. In general, differences in capabilities, such as computational power and interaction resources, lead to an heterogeneity that is sometimes positively referred to as device diversity but also, negatively, as device fragmentation. When designing applications for mobile devices, besides general rules and principles of usability, developers cope with further constraints. Restricted capabilities, due to display size, input modality and computational power, imply important design and implementation choices in order to guarantee usability. In addition, when the application is likely to be used by subjects affected by some impairment, the system has also to comply with accessibility requirements. The aim of this dissertation is to propose and discuss examples of such techniques, aimed to support user interfaces on mobile devices, by tackling design, development and evaluation of specific solutions for portable terminals as well as for enabling interoperability across diverse devices (including desktops, handhelds, smartphones). Usefulness and usability aspects are taken into great consideration by the main research questions that drove the activities of the study. With respect the such questions, the three central chapters of the dissertation are respectively aimed at evaluating: hardware/software solutions for edutainment and accessibility in mobile museum guides, visualization strategies for mobile users visiting smart environments, and techniques for user interface migration across diverse devices in multi-user contexts. Motivations, design, implementation and evaluation about a number of solutions aimed to support several dimensions of user interfaces for mobile devices are widely discussed throughout the dissertation, and some findings are drawn. Each one of the prototypes described in the following chapters has been entirely developed within the research activities of the laboratory where the author performed his PhD. Most activities were related to tasks of international research projects and the organization of this dissertation reflects their evolution chronology

An Investigation into Possible Attacks on HTML5 IndexedDB and their Prevention

This thesis presents an analysis of, and enhanced security model for IndexedDB, the persistent HTML5 browser-based data store. In versions of HTML prior to HTML5, web sites used cookies to track user preferences locally. Cookies are however limited both in file size and number, and must also be added to every HTTP request, which increases web traffic unnecessarily. Web functionality has however increased significantly since cookies were introduced by Netscape in 1994. Consequently, web developers require additional capabilities to keep up with the evolution of the World Wide Web and growth in eCommerce. The response to this requirement was the IndexedDB API, which became an official W3C recommendation in January 2015. The IndexedDB API includes an Object Store, indices, and cursors and so gives HTML5 - compliant browsers a transactional database capability. Furthermore, once downloaded, IndexedDB data stores do not require network connectivity. This permits mobile web- based applications to work without a data connection. Such IndexedDB data stores will be used to store customer data, they will inevitably become targets for attackers. This thesis firstly argues that the design of IndexedDB makes it unavoidably insecure. That is, every implementation is vulnerable to attacks such as Cross Site Scripting, and even data that has been deleted from databases may be stolen using appropriate software tools. This is demonstrated experimentally on both mobile and desktop browsers. IndexedDB is however capable of high performance even when compared to servers running optimized local databases. This is demonstrated through the development of a formal performance model. The performance predictions for IndexedDB were tested experimentally, and the results showed high conformance over a range of usage scenarios. This implies that IndexedDB is potentially a useful HTML5 API if the security issues can be addressed. In the final component of this thesis, we propose and implement enhancements that correct the security weaknesses identified in IndexedDB. The enhancements use multifactor authentication, and so are resistant to Cross Site Scripting attacks. This enhancement is then demonstrated experimentally, showing that HTML5 IndexedDB may be used securely both online and offline. This implies that secure, standards compliant browser based applications with persistent local data stores may both feasible and efficient

Study of stochastic and machine learning tecniques for anomaly-based Web atack detection

Mención Internacional en el título de doctorWeb applications are exposed to different threats and it is necessary to protect them. Intrusion Detection Systems (IDSs) are a solution external to the web application that do not require the modification of the application’s code in order to protect it. These systems are located in the network, monitoring events and searching for signs of anomalies or threats that can compromise the security of the information systems. IDSs have been applied to traffic analysis of different protocols, such as TCP, FTP or HTTP. Web Application Firewalls (WAFs) are special cases of IDSs that are specialized in analyzing HTTP traffic with the aim of safeguarding web applications. The increase in the amount of data traveling through the Internet and the growing sophistication of the attacks, make necessary protection mechanisms that are both effective and efficient. This thesis proposes three anomaly-based WAFs with the characteristics of being high-speed, reaching high detection results and having a simple design. The anomaly-based approach defines the normal behavior of web application. Actions that deviate from it are considered anomalous. The proposed WAFs work at the application layer analyzing the payload of HTTP requests. These systems are designed with different detection algorithms in order to compare their results and performance. Two of the systems proposed are based on stochastic techniques: one of them is based on statistical techniques and the other one in Markov chains. The third WAF presented in this thesis is ML-based. Machine Learning (ML) deals with constructing computer programs that automatically learn with experience and can be very helpful in dealing with big amounts of data. Concretely, this third WAF is based on decision trees given their proved effectiveness in intrusion detection. In particular, four algorithms are employed: C4.5, CART, Random Tree and Random Forest. Typically, two phases are distinguished in IDSs: preprocessing and processing. In the case of stochastic systems, preprocessing includes feature extraction. The processing phase consists in training the system in order to learn the normal behavior and later testing how well it classifies the incoming requests as either normal or anomalous. The detection models of the systems are implemented either with statistical techniques or with Markov chains, depending on the system considered. For the system based on decision trees, the preprocessing phase comprises feature extraction as well as feature selection. These two phases are optimized. On the one hand, new feature extraction methods are proposed. They combine features extracted by means of expert knowledge and n-grams, and have the capacity of improving the detection results of both techniques separately. For feature selection, the Generic Feature Selection GeFS measure has been used, which has been proven to be very effective in reducing the number of redundant and irrelevant features. Additionally, for the three systems, a study for establishing the minimum number of requests required to train them in order to achieve a certain detection result has been performed. Reducing the number of training requests can greatly help in the optimization of the resource consumption of WAFs as well as on the data gathering process. Besides designing and implementing the systems, evaluating them is an essential step. For that purpose, a dataset is necessary. Unfortunately, finding labeled and adequate datasets is not an easy task. In fact, the study of the most popular datasets in the intrusion detection field reveals that most of them do not satisfy the requirements for evaluating WAFs. In order to tackle this situation, this thesis proposes the new CSIC dataset, that satisfies the necessary conditions to satisfactorily evaluate WAFs. The proposed systems have been experimentally evaluated. For that, the proposed CSIC dataset and the existing ECML/PKDD dataset have been used. The three presented systems have been compared in terms of their detection results, processing time and number of training requests used. For this comparison, the CSIC dataset has been used. In summary, this thesis proposes three WAFs based on stochastic and ML techniques. Additionally, the systems are compared, what allows to determine which system is the most appropriate for each scenario.Las aplicaciones web están expuestas a diferentes amenazas y es necesario protegerlas. Los sistemas de detección de intrusiones (IDSs del inglés Intrusion Detection Systems) son una solución externa a la aplicación web que no requiere la modificación del código de la aplicación para protegerla. Estos sistemas se sitúan en la red, monitorizando los eventos y buscando señales de anomalías o amenazas que puedan comprometer la seguridad de los sistemas de información. Los IDSs se han aplicado al análisis de tráfico de varios protocolos, tales como TCP, FTP o HTTP. Los Cortafuegos de Aplicaciones Web (WAFs del inglés Web Application Firewall) son un caso especial de los IDSs que están especializados en analizar tráfico HTTP con el objetivo de salvaguardar las aplicaciones web. El incremento en la cantidad de datos circulando por Internet y la creciente sofisticación de los ataques hace necesario contar con mecanismos de protección que sean efectivos y eficientes. Esta tesis propone tres WAFs basados en anomalías que tienen las características de ser de alta velocidad, alcanzar altos resultados de detección y contar con un diseño sencillo. El enfoque basado en anomalías define el comportamiento normal de la aplicación, de modo que las acciones que se desvían del mismo se consideran anómalas. Los WAFs diseñados trabajan en la capa de aplicación y analizan el contenido de las peticiones HTTP. Estos sistemas están diseñados con diferentes algoritmos de detección para comparar sus resultados y rendimiento. Dos de los sistemas propuestos están basados en técnicas estocásticas: una de ellas está basada en técnicas estadísticas y la otra en cadenas de Markov. El tercer WAF presentado en esta tesis está basado en aprendizaje automático. El aprendizaje automático (ML del inglés Machine Learning) se ocupa de cómo construir programas informáticos que aprenden automáticamente con la experiencia y puede ser muy útil cuando se trabaja con grandes cantidades de datos. En concreto, este tercer WAF está basado en árboles de decisión, dada su probada efectividad en la detección de intrusiones. En particular, se han empleado cuatro algoritmos: C4.5, CART, Random Tree y Random Forest. Típicamente se distinguen dos fases en los IDSs: preprocesamiento y procesamiento. En el caso de los sistemas estocásticos, en la fase de preprocesamiento se realiza la extracción de características. El procesamiento consiste en el entrenamiento del sistema para que aprenda el comportamiento normal y más tarde se comprueba cuán bien el sistema es capaz de clasificar las peticiones entrantes como normales o anómalas. Los modelos de detección de los sistemas están implementados bien con técnicas estadísticas o bien con cadenas de Markov, dependiendo del sistema considerado. Para el sistema basado en árboles de decisión la fase de preprocesamiento comprende tanto la extracción de características como la selección de características. Estas dos fases se han optimizado. Por un lado, se proponen nuevos métodos de extracción de características. Éstos combinan características extraídas por medio de conocimiento experto y n-gramas y tienen la capacidad de mejorar los resultados de detección de ambas técnicas por separado. Para la selección de características, se ha utilizado la medida GeFS (del inglés Generic Feature Selection), la cual ha probado ser muy efectiva en la reducción del número de características redundantes e irrelevantes. Además, para los tres sistemas, se ha realizado un estudio para establecer el mínimo número de peticiones necesarias para entrenarlos y obtener un cierto resultado. Reducir el número de peticiones de entrenamiento puede ayudar en gran medida a la optimización del consumo de recursos de los WAFs así como en el proceso de adquisición de datos. Además de diseñar e implementar los sistemas, la tarea de evaluarlos es esencial. Para este propósito es necesario un conjunto de datos. Desafortunadamente, encontrar conjuntos de datos etiquetados y adecuados no es una tarea fácil. De hecho, el estudio de los conjuntos de datos más utilizados en el campo de la detección de intrusiones revela que la mayoría de ellos no cumple los requisitos para evaluar WAFs. Para enfrentar esta situación, esta tesis presenta un nuevo conjunto de datos llamado CSIC, que satisface las condiciones necesarias para evaluar WAFs satisfactoriamente. Los sistemas propuestos se han evaluado experimentalmente. Para ello, se ha utilizado el conjunto de datos propuesto (CSIC) y otro existente llamado ECML/PKDD. Los tres sistemas presentados se han comparado con respecto a sus resultados de detección, tiempo de procesamiento y número de peticiones de entrenamiento utilizadas. Para esta comparación se ha utilizado el conjunto de datos CSIC. En resumen, esta tesis propone tres WAFs basados en técnicas estocásticas y de ML. Además, se han comparado estos sistemas entre sí, lo que permite determinar qué sistema es el más adecuado para cada escenario.Este trabajo ha sido realizado en el marco de las becas predoctorales de la Junta de Amplicación de Estudios (JAE) de la Agencia Estatal Consejo Superior de Investigaciones Científicas (CSIC).Programa Oficial de Doctorado en Ciencia y Tecnología InformáticaPresidente: Luis Hernández Encinas.- Secretario: Juan Manuel Estévez Tapiador.- Vocal: Georg Carl