Leveraging cloud computing for IPTV : moving the set-top box to the cloud

Dissertação de mestrado em Engenharia de InformáticaP Television (IPTV) has changed the way we perceive our TV sets. It has given us the ability to take control of our TV viewing activities. We can now interact with our TV set in a way we might have thought impossible a few decades ago. With the push of a button we can schedule TV recordings, stop and even rewind the broadcast. Users are now able to control what they want to view, when they want to view it. TV sets have also evolved and gained a new set of functionalities, they have become smart, allowing us to connect o the Internet and decode media files that are inputed through usb ports and streamed through the network or the Internet. But the TV is no longer the center of our multimedia experience, now we have new ways to access the TV content we have subscribed. Viewing TV on a laptop, a smartphone or even a tablet is now more and more common. Cloud Computing (CC) has also brought us some revolutions, we can now have systems that grow and adapt on-the-fly to the conditions presented to them. An application can extend it’s storage capacity in a matter of minutes, and the same can be said of the processing power of the underlying platform. Our objective in this work is to discuss the possibility of creating a synergy between both services. By creating a system where IPTV and CC would interact, we could create a service that would provide IPTV anytime and anywhere. With the possibilities CC brings we can move the Set-top Box (STB) to the cloud and create new service functionalities and reduce the cost of having to install a STB in every client. We have focussed our work in the simulation of a CC infrastructure that would host Virtual STB’s that would be accessible from wherever there would be a network connection

An Approach to Guide Users Towards Less Revealing Internet Browsers

When browsing the Internet, HTTP headers enable both clients and servers send extra data in their requests or responses such as the User-Agent string. This string contains information related to the sender’s device, browser, and operating system. Previous research has shown that there are numerous privacy and security risks result from exposing sensitive information in the User-Agent string. For example, it enables device and browser fingerprinting and user tracking and identification. Our large analysis of thousands of User-Agent strings shows that browsers differ tremendously in the amount of information they include in their User-Agent strings. As such, our work aims at guiding users towards using less exposing browsers. In doing so, we propose to assign an exposure score to browsers based on the information they expose and vulnerability records. Thus, our contribution in this work is as follows: first, provide a full implementation that is ready to be deployed and used by users. Second, conduct a user study to identify the effectiveness and limitations of our proposed approach. Our implementation is based on using more than 52 thousand unique browsers. Our performance and validation analysis show that our solution is accurate and efficient. The source code and data set are publicly available and the solution has been deployed

Challenges and Solutions for Intrusion Detection in Wireless Mesh Networks

The problem of intrusion detection in wireless mesh networks (WMN) is challenging, primarily because of lack of single vantage points where traffic can be analyzed and the limited resources available to participating nodes. Although the problem has received some attention from the research community, little is known about the tradeoffs among different objectives, such as high network performance, low energy consumption, and high security effectiveness. In this research, we show how accurate intrusion detection can be achieved in such resource constrained environments. The major challenges that hinder the performance of intrusion detection systems (IDS) in WMN are resources (e.g., energy, processing, and storage capabilities) accompanied by the adhoc-dynamic communication flows. In light of these challenges, we classify the proposed solutions into four classes: 1) Resourceless Traffic Aware (RL-TW) IDS, 2) Resourceless Traffic Agnostic (RLTG) IDS, 3) Resourceful Traffic Agnostic (RF-TG) IDS, and 4) Resourceful Traffic Aware (RF-TW) IDS. To achieve a desirable level of intrusion detection in WMN, we propose a research program encompassing five thrusts. First we show how traffic-awareness helps IDS solutions achieving high detection rates in resource-constrained WMN. Next, we propose two RL-TG (i.e., cooperative and non-cooperative) IDS solutions that can optimally monitor the entire WMN traffic without relying on WMN traffic information. The third (RF-TG) and fourth (RF-TW) IDS solutions propose energy-efficient monitoring mechanisms for intrusion detection in battery-powered WMN for traffic-agnostic and traffic-aware scenarios, respectively. We then investigate the Attack and Fault Tolerance of our proposed solutions and finally enumerate potential improvements and future works for our proposed solutions