Measurement and management of the impact of mobility on low-latency anonymity networks

Privacy, including the right to privacy of correspondence, is a human right. Privacy-enhancing technologies, such as the Tor anonymity network, help maintain this right. The increasing use of Tor from mobile devices raises new challenges for the continued effectiveness of this low-latency anonymity network. Mobile Tor users may access the Internet from a range of wireless networks and service providers. Whenever a wireless network hands-off a mobile device’s connection from one access point to another, its external Internet Protocol (IP) address changes, and the connection to the Tor network is dropped. Every dropped connection requires the Tor circuit to be rebuilt. The time required to rebuild the circuit negatively impacts client performance. This research is the first to highlight this negative impact and to investigate the likely extent of the impact for typical usage scenarios and mobility models. The increased network churn caused by circuit rebuilding also negatively impacts anonymity. A novel metric (q-factor) is proposed here to measure the trade-off between anonymity and performance over the duration of a communication session. Two new solutions to the problems of managing mobility in a low-latency anonymity network are proposed in this thesis. The first solution relies on adaptive client throttling, based on a Kaplan-Meier estimator of the likelihood of a mobile network hand-off. The second solution relies on the use of a static bridge relay (mBridge) that acts as a persistent ‘home’ for a mobile Tor connection, so avoiding the need to recreate the Tor circuit whenever the mobile device is handed-off. The effectiveness of these solutions has been measured using the new q-factor metric. Both solutions provide better performance for mobile Tor clients than the standard Tor client implementation, although some performance reduction by comparison with static Tor clients remains. The bridge relay solution (mBridge) has been shown to offer better performance than client throttling, but is more vulnerable to certain types of attack. A strength of both solutions is that changes are restricted to client devices, the existing algorithms and protocols of the interior Tor network are unaffected

Evaluating Vulnerability Prediction Models

Today almost every device depends on a piece of software. As a result, our life increasingly depends on some software form such as smartphone apps, laundry machines, web applications, computers, transportation and many others, all of which rely on software. Inevitably, this dependence raises the issue of software vulnerabilities and their possible impact on our lifestyle. Over the years, researchers and industrialists suggested several approaches to detect such issues and vulnerabilities. A particular popular branch of such approaches, usually called Vulnerability Prediction Modelling (VPM) techniques, leverage prediction modelling techniques that flag suspicious (likely vulnerable) code components. These techniques rely on source code features as indicators of vulnerabilities to build the prediction models. However, the emerging question is how effective such methods are and how they can be used in practice. The present dissertation studies vulnerability prediction models and evaluates them on real and reliable playground. To this end, it suggests a toolset that automatically collects real vulnerable code instances, from major open source systems, suitable for applying VPM. These code instances are then used to analyze, replicate, compare and develop new VPMs. Specifically, the dissertation has 3 main axes: The first regards the analysis of vulnerabilities. Indeed, to build VPMs accurately, numerous data are required. However, by their nature, vulnerabilities are scarce and the information about them is spread over different sources (NVD, Git, Bug Trackers). Thus, the suggested toolset (develops an automatic way to build a large dataset) enables the reliable and relevant analysis of VPMs. The second axis focuses on the empirical comparison and analysis of existing Vulnerability Prediction Models. It thus develops and replicates existing VPMs. To this end, the thesis introduces a framework that builds, analyse and compares existing prediction models (using the already proposed sets of features) using the dataset developed on the first axis. The third axis explores the use of cross-entropy (metric used by natural language processing) as a potential feature for developing new VPMs. Cross-entropy, usually referred to as the naturalness of code, is a recent approach that measures the repetitiveness of code (relying on statistical models). Using cross-entropy, the thesis investigates different ways of building and using VPMs. Overall, this thesis provides a fully-fledge study on Vulnerability Prediction Models aiming at assessing and improving their performance

Edge Learning for 6G-enabled Internet of Things: A Comprehensive Survey of Vulnerabilities, Datasets, and Defenses

The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to enable future networks, such as sixth generation (6G) networks, to efficiently support sophisticated applications ranging from autonomous driving capabilities to the Metaverse. Edge learning is a new and powerful approach to training models across distributed clients while protecting the privacy of their data. This approach is expected to be embedded within future network infrastructures, including 6G, to solve challenging problems such as resource management and behavior prediction. This survey article provides a holistic review of the most recent research focused on edge learning vulnerabilities and defenses for 6G-enabled IoT. We summarize the existing surveys on machine learning for 6G IoT security and machine learning-associated threats in three different learning modes: centralized, federated, and distributed. Then, we provide an overview of enabling emerging technologies for 6G IoT intelligence. Moreover, we provide a holistic survey of existing research on attacks against machine learning and classify threat models into eight categories, including backdoor attacks, adversarial examples, combined attacks, poisoning attacks, Sybil attacks, byzantine attacks, inference attacks, and dropping attacks. In addition, we provide a comprehensive and detailed taxonomy and a side-by-side comparison of the state-of-the-art defense methods against edge learning vulnerabilities. Finally, as new attacks and defense technologies are realized, new research and future overall prospects for 6G-enabled IoT are discussed

Modern Socio-Technical Perspectives on Privacy

This open access book provides researchers and professionals with a foundational understanding of online privacy as well as insight into the socio-technical privacy issues that are most pertinent to modern information systems, covering several modern topics (e.g., privacy in social media, IoT) and underexplored areas (e.g., privacy accessibility, privacy for vulnerable populations, cross-cultural privacy). The book is structured in four parts, which follow after an introduction to privacy on both a technical and social level: Privacy Theory and Methods covers a range of theoretical lenses through which one can view the concept of privacy. The chapters in this part relate to modern privacy phenomena, thus emphasizing its relevance to our digital, networked lives. Next, Domains covers a number of areas in which privacy concerns and implications are particularly salient, including among others social media, healthcare, smart cities, wearable IT, and trackers. The Audiences section then highlights audiences that have traditionally been ignored when creating privacy-preserving experiences: people from other (non-Western) cultures, people with accessibility needs, adolescents, and people who are underrepresented in terms of their race, class, gender or sexual identity, religion or some combination. Finally, the chapters in Moving Forward outline approaches to privacy that move beyond one-size-fits-all solutions, explore ethical considerations, and describe the regulatory landscape that governs privacy through laws and policies. Perhaps even more so than the other chapters in this book, these chapters are forward-looking by using current personalized, ethical and legal approaches as a starting point for re-conceptualizations of privacy to serve the modern technological landscape. The book’s primary goal is to inform IT students, researchers, and professionals about both the fundamentals of online privacy and the issues that are most pertinent to modern information systems. Lecturers or teacherscan assign (parts of) the book for a “professional issues” course. IT professionals may select chapters covering domains and audiences relevant to their field of work, as well as the Moving Forward chapters that cover ethical and legal aspects. Academicswho are interested in studying privacy or privacy-related topics will find a broad introduction in both technical and social aspects

Detection of vulnerabilities and automatic protection for web applications

Tese de doutoramento, Informática (Ciências da Computação), Universidade de Lisboa, Faculdade de Ciências, 2016In less than three decades of existence, the Web evolved from a platform for accessing hypermedia to a framework for running complex web applications. These applications appear in many forms, from small home-made to large-scale commercial services such as Gmail, Office 365, and Facebook. Although a significant research effort on web application security has been on going for a while, these applications have been a major source of problems and their security continues to be challenged. An important part of the problem derives from vulnerable source code, often written in unsafe languages like PHP, and programmed by people without the appropriate knowledge about secure coding, who leave flaws in the applications. Nowadays the most exploited vulnerability category is the input validation, which is directly related with the user inputs inserted in web application forms. The thesis proposes methodologies and tools for the detection of input validation vulnerabilities in source code and for the protection of web applications written in PHP, using source code static analysis, machine learning and runtime protection techniques. An approach based on source code static analysis is used to identify vulnerabilities in applications programmed with PHP. The user inputs are tracked with taint analysis to determine if they reach a PHP function susceptible to be exploited. Then, machine learning is applied to determine if the identified flaws are actually vulnerabilities. In the affirmative case, the results of static analysis are used to remove the flaws, correcting the source code automatically thus protecting the web application. A new technique for source code static analysis is suggested to automatically learn about vulnerabilities and then to detect them. Machine learning applied to natural language processing is used to, in a first instance, learn characteristics about flaws in the source code, classifying it as being vulnerable or not, and then discovering and identifying the vulnerabilities. A runtime protection technique is also proposed to flag and block injection attacks against databases. The technique is implemented inside the database management system to improve the effectiveness of the detection of attacks, avoiding a semantic mismatch. Source code identifiers are employed so that, when an attack is flagged, the vulnerability is localized in the source code. Overall this work allowed the identification of about 1200 vulnerabilities in open source web applications available in the Internet, 560 of which previously unknown. The unknown vulnerabilities were reported to the corresponding software developers and most of them have already been removed.Em duas décadas de existência, a web evoluiu de uma plataforma para aceder a conteúdos hipermédia para uma infraestrutura para execução de aplicações complexas. Estas aplicações têm várias formas, desde aplicações pequenas e caseiras, a aplicações complexas e de grande escala e para diversos propósitos, como por exemplo serviços comerciais como o Gmail, Office 365 e Facebook. Apesar do grande esforço de investigação da última década em como tornar as aplicações web seguras, estas continuam a ser uma fonte de problemas e a sua segurança um desafio. Uma parte importante deste problema deriva de código fonte vulnerável, muitas vezes desenvolvido com linguagens de programação com poucas validações e construído por pessoas sem os conhecimentos mais adequados para uma programação segura. Atualmente a categoria de vulnerabilidades mais explorada é a de validação de input, diretamente relacionada com os dados (inputs) que os utilizadores inserem nas aplicações web. A tese propõe metodologias para a detecção e remoção de vulnerabilidades no código fonte e para a proteção das aplicações web em tempo de execução, empregando técnicas como a análise estática de código, aprendizagem máquina e protecção em tempo de execução. Numa primeira fase, a análise estática é utilizada para descobrir e identificar vulnerabilidades no código programado na linguagem PHP. Os inputs dos utilizadores são rastreados e é verificado se estes são parâmetros de funções PHP susceptíveis de serem exploradas. A combinação desta técnica com a aprendizagem máquina aplicada em minerização de dados é proposta para prever se as vulnerabilidades detectadas são falsos positivos ou reais. Caso sejam reais, o resultado da análise estática de código é utilizado para eliminá-las, corrigindo o código fonte automaticamente com fixes (remendos) e protegendo assim as aplicações web. A tese apresenta também uma nova técnica de análise estática de código para descobrir vulnerabilidades. A técnica aprende o que é código vulnerável e depois tira partido desse conhecimento para localizar problemas. A aprendizagem máquina aplicada ao processamento de linguagem natural é utilizada para, numa primeira instância, aprender aspectos que caracterizam as vulnerabilidades, para depois processar e analisar o código fonte, classificando-o como sendo ou não vulnerável, descobrindo e identificando os erros. Numa terceira fase, é proposta uma nova técnica de proteção em tempo de execução para descobrir e bloquear ataques de injeção contra bases de dados. A técnica é concretizada dentro do sistema de gestão de bases de dados para melhorar a eficácia na detecção dos ataques. É utilizada conjuntamente com identificadores de código fonte que, quando um ataque é sinalizado, permitem identificar a vulnerabilidade no programa. No total este trabalho permitiu a identificação de cerca de 1200 vulnerabilidades em aplicações web de código aberto disponíveis na Internet, das quais 560 eram até então desconhecidas. As vulnerabilidades desconhecidas foram reportadas aos autores do software onde foram encontradas e muitas delas já foram removidas

Modern Socio-Technical Perspectives on Privacy

This open access book provides researchers and professionals with a foundational understanding of online privacy as well as insight into the socio-technical privacy issues that are most pertinent to modern information systems, covering several modern topics (e.g., privacy in social media, IoT) and underexplored areas (e.g., privacy accessibility, privacy for vulnerable populations, cross-cultural privacy). The book is structured in four parts, which follow after an introduction to privacy on both a technical and social level: Privacy Theory and Methods covers a range of theoretical lenses through which one can view the concept of privacy. The chapters in this part relate to modern privacy phenomena, thus emphasizing its relevance to our digital, networked lives. Next, Domains covers a number of areas in which privacy concerns and implications are particularly salient, including among others social media, healthcare, smart cities, wearable IT, and trackers. The Audiences section then highlights audiences that have traditionally been ignored when creating privacy-preserving experiences: people from other (non-Western) cultures, people with accessibility needs, adolescents, and people who are underrepresented in terms of their race, class, gender or sexual identity, religion or some combination. Finally, the chapters in Moving Forward outline approaches to privacy that move beyond one-size-fits-all solutions, explore ethical considerations, and describe the regulatory landscape that governs privacy through laws and policies. Perhaps even more so than the other chapters in this book, these chapters are forward-looking by using current personalized, ethical and legal approaches as a starting point for re-conceptualizations of privacy to serve the modern technological landscape. The book’s primary goal is to inform IT students, researchers, and professionals about both the fundamentals of online privacy and the issues that are most pertinent to modern information systems. Lecturers or teacherscan assign (parts of) the book for a “professional issues” course. IT professionals may select chapters covering domains and audiences relevant to their field of work, as well as the Moving Forward chapters that cover ethical and legal aspects. Academicswho are interested in studying privacy or privacy-related topics will find a broad introduction in both technical and social aspects