Caracterização de tráfego de serviços de streaming em dispositivos móveis

Dissertação de mestrado integrado em Engenharia InformáticaNo contexto atual, o contínuo desenvolvimento tecnológico permite um acesso fácil e rápido a variadíssimos serviços e plataformas, por meio de dispositivos móveis. Por este motivo, o volume e diversidade de tráfego tem crescido de forma exponencial também. O conhecimento do tráfego que circula nas redes atuais torna-se indispensável, seja para ajudar a melhorar a gestão e configuração dos elementos e serviços de rede, seja para os utilizadores terem a oportunidade de gerir melhor os recursos na utilização dos seus dispositivos móveis e aplicações. Assim, este trabalho pretende aprofundar o estudo das características do tráfego gerado por dispositivos móveis, no acesso a determinadas plataformas/serviços. Também se espera incluir uma componente de Machine Learning para previsão da experiência do utilizador. Além disso, pretende-se obter base de comparação entre as versões web e aplicacional de um mesmo serviço.In the current context, continuous technological development allows easy and fast access to a wide range of services and platforms, through mobile devices. For this reason, the volume and diversity of traffic have grown exponentially as well. Knowledge of the traffic circulating in current networks is essential, either to help improving the management and configuration of the network elements and services, or for users to have the opportunity to better manage resources when using their mobile devices and applications. Thus, this work intends to deepen the study of the characteristics of the traffic generated by mobile devices when accessing certain platforms/services. It is also expected to include a Machine Learning component to predict the user experience. In addition, it is intended to obtain a basis for comparison between the web and application versions of the same service

The Dark Side(-Channel) of Mobile Devices: A Survey on Network Traffic Analysis

In recent years, mobile devices (e.g., smartphones and tablets) have met an increasing commercial success and have become a fundamental element of the everyday life for billions of people all around the world. Mobile devices are used not only for traditional communication activities (e.g., voice calls and messages) but also for more advanced tasks made possible by an enormous amount of multi-purpose applications (e.g., finance, gaming, and shopping). As a result, those devices generate a significant network traffic (a consistent part of the overall Internet traffic). For this reason, the research community has been investigating security and privacy issues that are related to the network traffic generated by mobile devices, which could be analyzed to obtain information useful for a variety of goals (ranging from device security and network optimization, to fine-grained user profiling). In this paper, we review the works that contributed to the state of the art of network traffic analysis targeting mobile devices. In particular, we present a systematic classification of the works in the literature according to three criteria: (i) the goal of the analysis; (ii) the point where the network traffic is captured; and (iii) the targeted mobile platforms. In this survey, we consider points of capturing such as Wi-Fi Access Points, software simulation, and inside real mobile devices or emulators. For the surveyed works, we review and compare analysis techniques, validation methods, and achieved results. We also discuss possible countermeasures, challenges and possible directions for future research on mobile traffic analysis and other emerging domains (e.g., Internet of Things). We believe our survey will be a reference work for researchers and practitioners in this research field.Comment: 55 page

Human-Computer Interaction: Security Aspects

Along with the rapid development of intelligent information age, users are having a growing interaction with smart devices. Such smart devices are interconnected together in the Internet of Things (IoT). The sensors of IoT devices collect information about users' behaviors from the interaction between users and devices. Since users interact with IoT smart devices for the daily communication and social network activities, such interaction generates a huge amount of network traffic. Hence, users' behaviors are playing an important role in the security of IoT smart devices, and the security aspects of Human-Computer Interaction are becoming significant. In this dissertation, we provide a threefold contribution: (1) we review security challenges of HCI-based authentication, and design a tool to detect deceitful users via keystroke dynamics; (2) we present the impact of users' behaviors on network traffic, and propose a framework to manage such network traffic; (3) we illustrate a proposal for energy-constrained IoT smart devices to be resilient against energy attack and efficient in network communication. More in detail, in the first part of this thesis, we investigate how users' behaviors impact on the way they interact with a device. Then we review the work related to security challenges of HCI-based authentication on smartphones, and Brain-Computer Interfaces (BCI). Moreover, we design a tool to assess the truthfulness of the information that users input using a computer keyboard. This tool is based on keystroke dynamics and it relies on machine learning technique to achieve this goal. To the best of our knowledge, this is the first work that associates the typing users' behaviors with the production of deceptive personal information. We reached an overall accuracy of 76% in the classification of a single answer as truthful or deceptive. In the second part of this thesis, we review the analysis of network traffic, especially related to the interaction between mobile devices and users. Since the interaction generates a huge amount of network traffic, we propose an innovative framework, GolfEngine, to manage and control the impact of users behavior on the network relying on Software Defined Networking (SDN) techniques. GolfEngine provides users a tool to build their security applications and offers Graphical User Interface (GUI) for managing and monitoring the network. In particular, GolfEngine provides the function of checking policy conflicts when users design security applications and the mechanism to check data storage redundancy. GolfEngine not only prevents the malicious inputting policies but also it enforces the security about network management of network traffic. The results of our simulation underline that GolfEngine provides an efficient, secure, and robust performance for managing network traffic via SDN. In the third and last part of this dissertation, we analyze the security aspects of battery-equipped IoT devices from the energy consumption perspective. Although most of the energy consumption of IoT devices is due to user interaction, there is still a significant amount of energy consumed by point-to-point communication and IoT network management. In this scenario, an adversary may hijack an IoT device and conduct a Denial of Service attack (DoS) that aims to run out batteries of other devices. Therefore, we propose EnergIoT, a novel method based on energetic policies that prevent such attacks and, at the same time, optimizes the communication between users and IoT devices, and extends the lifetime of the network. EnergIoT relies on a hierarchical clustering approach, based on different duty cycle ratios, to maximize network lifetime of energy-constrained smart devices. The results show that EnergIoT enhances the security and improves the network lifetime by 32%, compared to the earlier used approach, without sacrificing the network performance (i.e., end-to-end delay)