Amoeba: Circumventing ML-supported Network Censorship via Adversarial Reinforcement Learning

Embedding covert streams into a cover channel is a common approach to circumventing Internet censorship, due to censors' inability to examine encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.). However, recent advances in machine learning (ML) enable detecting a range of anti-censorship systems by learning distinct statistical patterns hidden in traffic flows. Therefore, designing obfuscation solutions able to generate traffic that is statistically similar to innocuous network activity, in order to deceive ML-based classifiers at line speed, is difficult. In this paper, we formulate a practical adversarial attack strategy against flow classifiers as a method for circumventing censorship. Specifically, we cast the problem of finding adversarial flows that will be misclassified as a sequence generation task, which we solve with Amoeba, a novel reinforcement learning algorithm that we design. Amoeba works by interacting with censoring classifiers without any knowledge of their model structure, but by crafting packets and observing the classifiers' decisions, in order to guide the sequence generation process. Our experiments using data collected from two popular anti-censorship systems demonstrate that Amoeba can effectively shape adversarial flows that have on average 94% attack success rate against a range of ML algorithms. In addition, we show that these adversarial flows are robust in different network environments and possess transferability across various ML models, meaning that once trained against one, our agent can subvert other censoring classifiers without retraining

Censorship Resistance as a Side-Effect

This position paper presents the following thought experiment: can we build communication protocols that (1) are sufficiently useful that they achieve widespread adoption as general-purpose communication mechanisms and (2) thwart censorship as a consequence of their design? We posit that a useful communication platform that is inherently resistant to traffic analysis, if widely adopted and used primarily for purposes not related to censorship circumvention, may be too politically and economically costly for a government to block.

Empowering bystanders to facilitate Internet censorship measurement and circumvention

Free and open exchange of information on the Internet is at risk: more than 60 countries practice some form of Internet censorship, and both the number of countries practicing censorship and the proportion of Internet users who are subject to it are on the rise. Understanding and mitigating these threats to Internet freedom is a continuous technological arms race with many of the most influential governments and corporations. By its very nature, Internet censorship varies drastically from region to region, which has impeded nearly all efforts to observe and fight it on a global scale. Researchers and developers in one country may find it very difficult to study censorship in another; this is particularly true for those in North America and Europe attempting to study notoriously pervasive censorship in Asia and the Middle East. This dissertation develops techniques and systems that empower users in one country, or bystanders, to assist in the measurement and circumvention of Internet censorship in another. Our work builds from the observation that there are people everywhere who are willing to help us if only they knew how. First, we develop Encore, which allows webmasters to help study Web censorship by collecting measurements from their sites' visitors. Encore leverages weaknesses in cross-origin security policy to collect measurements from a far more diverse set of vantage points than previously possible. Second, we build Collage, a technique that uses the pervasiveness and scalability of user-generated content to disseminate censored content. Collage's novel communication model is robust against censorship that is significantly more powerful than governments use today. Together, Encore and Collage help people everywhere study and circumvent Internet censorship.Ph.D

Security Hazards when Law is Code.

As software continues to eat the world, there is an increasing pressure to automate every aspect of society, from self-driving cars, to algorithmic trading on the stock market. As this pressure manifests into software implementations of everything, there are security concerns to be addressed across many areas. But are there some domains and fields that are distinctly susceptible to attacks, making them difficult to secure? My dissertation argues that one domain in particular—public policy and law— is inherently difficult to automate securely using computers. This is in large part because law and policy are written in a manner that expects them to be flexibly interpreted to be fair or just. Traditionally, this interpreting is done by judges and regulators who are capable of understanding the intent of the laws they are enforcing. However, when these laws are instead written in code, and interpreted by a machine, this capability to understand goes away. Because they blindly fol- low written rules, computers can be tricked to perform actions counter to their intended behavior. This dissertation covers three case studies of law and policy being implemented in code and security vulnerabilities that they introduce in practice. The first study analyzes the security of a previously deployed Internet voting system, showing how attackers could change the outcome of elections carried out online. The second study looks at airport security, investigating how full-body scanners can be defeated in practice, allowing attackers to conceal contraband such as weapons or high explosives past airport checkpoints. Finally, this dissertation also studies how an Internet censorship system such as China’s Great Firewall can be circumvented by techniques that exploit the methods employed by the censors themselves. To address these concerns of securing software implementations of law, a hybrid human-computer approach can be used. In addition, systems should be designed to allow for attacks or mistakes to be retroactively undone or inspected by human auditors. By combining the strengths of computers (speed and cost) and humans (ability to interpret and understand), systems can be made more secure and more efficient than a method employing either alone.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120795/1/ewust_1.pd

Automating the Discovery of Censorship Evasion Strategies

Censoring nation-states deploy complex network infrastructure to regulate what content citizens can access, and such restrictions to open sharing of information threaten the freedoms of billions of users worldwide, especially marginalized groups. Researchers and censoring regimes have long engaged in a cat-and-mouse game, leading to increasingly sophisticated Internet-scale censorship techniques and methods to evade them. In this dissertation, I study the technology that underpins this Internet censorship: middleboxes (e.g. firewalls). I argue the following thesis: It is possible to automatically discover packet sequence modifications that render deployed censorship middleboxes ineffective across multiple application-layer protocols. To evaluate this thesis, I develop Geneva, a novel genetic algorithm that discovers packet-manipulation-based censorship evasion strategies automatically against nation-state level censors. Training directly against a live adversary, Geneva com- poses, mutates, and evolves sophisticated strategies out of four basic packet manipulation primitives (drop, tamper, duplicate, and fragment). I show that Geneva can be effective across different application layer protocols (HTTP, HTTPS+SNI, HTTPS+ESNI, DNS, SMTP, FTP), censoring regimes (China, Iran, India, and Kazakhstan), and deployment contexts (client-side, server- side), even in cases where multiple middleboxes work in parallel to perform censorship. In total, I present 112 client-side strategies (85 of which work by modifying application layer data), and the first ever server-side strategies (11 in total). Finally, I use Geneva to discover two novel attacks that show censoring middleboxes can be weaponized to launch attacks against innocent hosts anywhere on the Internet. Collectively, my work shows that censorship evasion can be automated and that censorship infrastructures pose a greater threat to Internet availability than previously understood

An investigation into the efficacy of URL content filtering systems

Content filters are used to restrict to restrict minors from accessing to online content deemed inappropriate. While much research and evaluation has been done on the efficiency of content filters, there is little in the way of empirical research as to their efficacy. The accessing of inappropriate material by minors, and the role content filtering systems can play in preventing the accessing of inappropriate material, is largely assumed with little or no evidence. This thesis investigates if a content filter implemented with the stated aim of restricting specific Internet content from high school students achieved the goal of stopping students from accessing the identified material. The case is of a high school in Western Australia where the logs of a proxy content filter that included all Internet traffic requested by students were examined to determine the efficacy of the content filter. Using text extraction and pattern matching techniques to look for evidence of access to restricted content within this study, the results demonstrate that the belief that content filtering systems reliably prevent access to restricted content is misplaced. in this study there is direct evidence of circumvention of the content filter. This is single case study in one school and as such, the results are not generalisable to all schools or even through subsequent systems that replaced the content filter examined in this study, but it does raise the issue of the ability of these content filter systems to restrict content from high school students. Further studies across multiple schools and more complex circumvention methods would be required to identify if circumvention of content filters is a widespread issue

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

Discovering Network Control Vulnerabilities and Policies in Evolving Networks

The range and number of new applications and services are growing at an unprecedented rate. Computer networks need to be able to provide connectivity for these services and meet their constantly changing demands. This requires not only support of new network protocols and security requirements, but often architectural redesigns for long-term improvements to efficiency, speed, throughput, cost, and security. Networks are now facing a drastic increase in size and are required to carry a constantly growing amount of heterogeneous traffic. Unfortunately such dynamism greatly complicates security of not only the end nodes in the network, but also of the nodes of the network itself. To make matters worse, just as applications are being developed at faster and faster rates, attacks are becoming more pervasive and complex. Networks need to be able to understand the impact of these attacks and protect against them. Network control devices, such as routers, firewalls, censorship devices, and base stations, are elements of the network that make decisions on how traffic is handled. Although network control devices are expected to act according to specifications, there can be various reasons why they do not in practice. Protocols could be flawed, ambiguous or incomplete, developers could introduce unintended bugs, or attackers may find vulnerabilities in the devices and exploit them. Malfunction could intentionally or unintentionally threaten the confidentiality, integrity, and availability of end nodes and the data that passes through the network. It can also impact the availability and performance of the control devices themselves and the security policies of the network. The fast-paced evolution and scalability of current and future networks create a dynamic environment for which it is difficult to develop automated tools for testing new protocols and components. At the same time, they make the function of such tools vital for discovering implementation flaws and protocol vulnerabilities as networks become larger and more complex, and as new and potentially unrefined architectures become adopted. This thesis will present the design, implementation, and evaluation of a set of tools designed for understanding implementation of network control nodes and how they react to changes in traffic characteristics as networks evolve. We will first introduce Firecycle, a test bed for analyzing the impact of large-scale attacks and Machine-to-Machine (M2M) traffic on the Long Term Evolution (LTE) network. We will then discuss Autosonda, a tool for automatically discovering rule implementation and finding triggering traffic features in censorship devices. This thesis provides the following contributions: 1. The design, implementation, and evaluation of two tools to discover models of network control nodes in two scenarios of evolving networks, mobile network and censored internet 2. First existing test bed for analysis of large-scale attacks and impact of traffic scalability on LTE mobile networks 3. First existing test bed for LTE networks that can be scaled to arbitrary size and that deploys traffic models based on real traffic traces taken from a tier-1 operator 4. An analysis of traffic models of various categories of Internet of Things (IoT) devices 5. First study demonstrating the impact of M2M scalability and signaling overload on the packet core of LTE mobile networks 6. A specification for modeling of censorship device decision models 7. A means for automating the discovery of features utilized in censorship device decision models, comparison of these models, and their rule discover

Monitoring Internet censorship: the case of UBICA

As a consequence of the recent debate about restrictions in the access to content on the Internet, a strong motivation has arisen for censorship monitoring: an independent, publicly available and global watch on Internet censorship activities is a necessary goal to be pursued in order to guard citizens' right of access to information. Several techniques to enforce censorship on the Internet are known in literature, differing in terms of transparency towards the user, selectivity in blocking specific resources or whole groups of services, collateral effects outside the administrative borders of their intended application. Monitoring censorship is also complicated by the dynamic nature of multiple aspects of this phenomenon, the number and diversity of resources targeted by censorship and its global scale. In the present Thesis an analysis of literature on internet censorship and available solutions for censorship detection has been performed, characterizing censorship enforcement techniques and censorship detection techniques and tools. The available platforms and tools for censorship detection have been found falling short of providing a comprehensive monitoring platform able to manage a diverse set of measurement vantage points and a reporting interface continuously updated with the results of automated censorship analysis. The candidate proposes a design of such a platform, UBICA, along with a prototypical implementation whose effectiveness has been experimentally validated in global monitoring campaigns. The results of the validation are discussed, confirming the effectiveness of the proposed design and suggesting future enhancements and research