Raising the Datagram API to Support Transport Protocol Evolution

Some application developers can wield huge resources to build new transport protocols, for these developers the present UDP Socket API is perfectly fine. They have access to large test beds and sophisticated tools. Many developers do not have these resources. This paper presents a new high-level Datagram API that is for everyone else, this has an advantage of offering a clear evolutionary path to support new requirements. This new API is needed to move forward the base of the system, allowing developers with limited resources to evolve their applications while accessing new network services

Large-Scale Measurement of Real-Time Communication on the Web

Web Real-Time Communication (WebRTC) is getting wide adoptions across the browsers (Chrome, Firefox, Opera, etc.) and platforms (PC, Android, iOS). It enables application developers to add real-time communications features (text chat, audio/video calls) to web applications using W3C standard JavaScript APIs, and the end users can enjoy real-time multimedia communication experience from the browser without the complication of installing special applications or browser plug-ins. As WebRTC based applications are getting deployed on the Internet by thousands of companies across the globe, it is very important to understand the quality of the real-time communication services provided by these applications. Important performance metrics to be considered include: whether the communication session was properly setup, what are the network delays, packet loss rate, throughput, etc. At Callstats.io, we provide a solution to address the above concerns. By integrating an JavaScript API into WebRTC applications, Callstats.io helps application providers to measure the Quality of Experience (QoE) related metrics on the end user side. This thesis illustrates how this WebRTC performance measurement system is designed and built and we show some statistics derived from the collected data to give some insight into the performance of today’s WebRTC based real-time communication services. According to our measurement, real-time communication over the Internet are generally performing well in terms of latency and loss. The throughput are good for about 30% of the communication sessions

Design and implement a new mechanism for audio, video and screen recording based on WebRTC technology

Many years ago, Flash was essential in browsers to interact with the user media devices, such as a microphone and camera. Today, Web Real-Time Communication (WebRTC) technology has come to substitute the flash, so browsers do not need the flash to access media devices or establish their communication. However, WebRTC standards do not express precisely how browsers can record audios, videos or screen instead of describing getUserMedia API that enables a browser to access microphone and camera. The prime objective of this research is to create a new WebRTC recording mechanism to record audios, videos, and screen using Google Chrome, Firefox, and Opera. This experiment applied through Ethernet and Wireless of the Internet and 4G networks. Also, the recording mechanism of this research was obtained based on JavaScript Library for audio, video, screen (2D and 3D animation) recording. Besides, different audio and video codecs in Chrome, Firefox and Opera were utilised, such as VP8, VP9, and H264 for video, and Opus codec for audio. Not only but also, various bitrates (100 bytes bps, 1 Kbps, 100 Kbps, 1 MB bps, and 1 GB bps), different resolutions (1080p, 720p, 480p, and HD (3840* 2160)), and various frame-rates (fps) 5, 15, 24, 30 and 60 were considered and tested. Besides, an evaluation of recording mechanism, Quality of Experience (QoE) through actual users, resources, such as CPU performance was also done. In this paper, a novel implementation was accomplished over different networks, different browsers, various audio and video codecs, many peers, opening one or multi browsers at the same time, keep the streaming active as much as the user needs, save the record, using only audio and/or video recording as conferencing with full screen, etc

TOWARDS RELIABLE CIRCUMVENTION OF INTERNET CENSORSHIP

The Internet plays a crucial role in today\u27s social and political movements by facilitating the free circulation of speech, information, and ideas; democracy and human rights throughout the world critically depend on preserving and bolstering the Internet\u27s openness. Consequently, repressive regimes, totalitarian governments, and corrupt corporations regulate, monitor, and restrict the access to the Internet, which is broadly known as Internet \emph{censorship}. Most countries are improving the internet infrastructures, as a result they can implement more advanced censoring techniques. Also with the advancements in the application of machine learning techniques for network traffic analysis have enabled the more sophisticated Internet censorship. In this thesis, We take a close look at the main pillars of internet censorship, we will introduce new defense and attacks in the internet censorship literature. Internet censorship techniques investigate users’ communications and they can decide to interrupt a connection to prevent a user from communicating with a specific entity. Traffic analysis is one of the main techniques used to infer information from internet communications. One of the major challenges to traffic analysis mechanisms is scaling the techniques to today\u27s exploding volumes of network traffic, i.e., they impose high storage, communications, and computation overheads. We aim at addressing this scalability issue by introducing a new direction for traffic analysis, which we call \emph{compressive traffic analysis}. Moreover, we show that, unfortunately, traffic analysis attacks can be conducted on Anonymity systems with drastically higher accuracies than before by leveraging emerging learning mechanisms. We particularly design a system, called \deepcorr, that outperforms the state-of-the-art by significant margins in correlating network connections. \deepcorr leverages an advanced deep learning architecture to \emph{learn} a flow correlation function tailored to complex networks. Also to be able to analyze the weakness of such approaches we show that an adversary can defeat deep neural network based traffic analysis techniques by applying statistically undetectable \emph{adversarial perturbations} on the patterns of live network traffic. We also design techniques to circumvent internet censorship. Decoy routing is an emerging approach for censorship circumvention in which circumvention is implemented with help from a number of volunteer Internet autonomous systems, called decoy ASes. We propose a new architecture for decoy routing that, by design, is significantly stronger to rerouting attacks compared to \emph{all} previous designs. Unlike previous designs, our new architecture operates decoy routers only on the downstream traffic of the censored users; therefore we call it \emph{downstream-only} decoy routing. As we demonstrate through Internet-scale BGP simulations, downstream-only decoy routing offers significantly stronger resistance to rerouting attacks, which is intuitively because a (censoring) ISP has much less control on the downstream BGP routes of its traffic. Then, we propose to use game theoretic approaches to model the arms races between the censors and the censorship circumvention tools. This will allow us to analyze the effect of different parameters or censoring behaviors on the performance of censorship circumvention tools. We apply our methods on two fundamental problems in internet censorship. Finally, to bring our ideas to practice, we designed a new censorship circumvention tool called \name. \name aims at increasing the collateral damage of censorship by employing a ``mass\u27\u27 of normal Internet users, from both censored and uncensored areas, to serve as circumvention proxies