531 research outputs found
An Empirical Study of the I2P Anonymity Network and its Censorship Resistance
Tor and I2P are well-known anonymity networks used by many individuals to
protect their online privacy and anonymity. Tor's centralized directory
services facilitate the understanding of the Tor network, as well as the
measurement and visualization of its structure through the Tor Metrics project.
In contrast, I2P does not rely on centralized directory servers, and thus
obtaining a complete view of the network is challenging. In this work, we
conduct an empirical study of the I2P network, in which we measure properties
including population, churn rate, router type, and the geographic distribution
of I2P peers. We find that there are currently around 32K active I2P peers in
the network on a daily basis. Of these peers, 14K are located behind NAT or
firewalls.
Using the collected network data, we examine the blocking resistance of I2P
against a censor that wants to prevent access to I2P using address-based
blocking techniques. Despite the decentralized characteristics of I2P, we
discover that a censor can block more than 95% of peer IP addresses known by a
stable I2P client by operating only 10 routers in the network. This amounts to
severe network impairment: a blocking rate of more than 70% is enough to cause
significant latency in web browsing activities, while blocking more than 90% of
peer IP addresses can make the network unusable. Finally, we discuss the
security consequences of the network being blocked, and directions for
potential approaches to make I2P more resistant to blocking.Comment: 14 pages, To appear in the 2018 Internet Measurement Conference
(IMC'18
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
QUICstep: Circumventing QUIC-based Censorship
Governments around the world limit free and open communication on the
Internet through censorship. To reliably identify and block access to certain
web domains, censors inspect the plaintext TLS SNI field sent in TLS
handshakes. With QUIC rapidly displacing TCP as the dominant transport-layer
protocol on the web, censorship regimes have already begun prosecuting network
traffic delivered over QUIC. With QUIC censorship poised to expand, censorship
circumvention tools must similarly adapt. We present QUICstep, a
censorship-resilient, application-agnostic, performant, and easy-to-implement
approach to censorship circumvention in the QUIC era. QUICstep circumvents TLS
SNI censorship by conducting a QUIC-TLS handshake over an encrypted tunnel to
hide the SNI field from censors and performs connection migration to resume the
QUIC session in plain sight of the censor. Our evaluation finds that QUICstep
successfully establishes QUIC sessions in the presence of a proof-of-concept
censor with minimal latency overhead
Using Botnet Technologies to Counteract Network Traffic Analysis
Botnets have been problematic for over a decade. They are used to launch malicious activities including DDoS (Distributed-Denial-of-Service), spamming, identity theft, unauthorized bitcoin mining and malware distribution. A recent nation-wide DDoS attacks caused by the Mirai botnet on 10/21/2016 involving 10s of millions of IP addresses took down Twitter, Spotify, Reddit, The New York Times, Pinterest, PayPal and other major websites. In response to take-down campaigns by security personnel, botmasters have developed technologies to evade detection. The most widely used evasion technique is DNS fast-flux, where the botmaster frequently changes the mapping between domain names and IP addresses of the C&C server so that it will be too late or too costly to trace the C&C server locations. Domain names generated with Domain Generation Algorithms (DGAs) are used as the \u27rendezvous\u27 points between botmasters and bots. This work focuses on how to apply botnet technologies (fast-flux and DGA) to counteract network traffic analysis, therefore protecting user privacy. A better understanding of botnet technologies also helps us be pro-active in defending against botnets. First, we proposed two new DGAs using hidden Markov models (HMMs) and Probabilistic Context-Free Grammars (PCFGs) which can evade current detection methods and systems. Also, we developed two HMM-based DGA detection methods that can detect the botnet DGA-generated domain names with/without training sets. This helps security personnel understand the botnet phenomenon and develop pro-active tools to detect botnets. Second, we developed a distributed proxy system using fast-flux to evade national censorship and surveillance. The goal is to help journalists, human right advocates and NGOs in West Africa to have a secure and free Internet. Then we developed a covert data transport protocol to transform arbitrary message into real DNS traffic. We encode the message into benign-looking domain names generated by an HMM, which represents the statistical features of legitimate domain names. This can be used to evade Deep Packet Inspection (DPI) and protect user privacy in a two-way communication. Both applications serve as examples of applying botnet technologies to legitimate use. Finally, we proposed a new protocol obfuscation technique by transforming arbitrary network protocol into another (Network Time Protocol and a video game protocol of Minecraft as examples) in terms of packet syntax and side-channel features (inter-packet delay and packet size). This research uses botnet technologies to help normal users have secure and private communications over the Internet. From our botnet research, we conclude that network traffic is a malleable and artificial construct. Although existing patterns are easy to detect and characterize, they are also subject to modification and mimicry. This means that we can construct transducers to make any communication pattern look like any other communication pattern. This is neither bad nor good for security. It is a fact that we need to accept and use as best we can
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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
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