6,322 research outputs found
Systemization of Pluggable Transports for Censorship Resistance
An increasing number of countries implement Internet censorship at different
scales and for a variety of reasons. In particular, the link between the
censored client and entry point to the uncensored network is a frequent target
of censorship due to the ease with which a nation-state censor can control it.
A number of censorship resistance systems have been developed thus far to help
circumvent blocking on this link, which we refer to as link circumvention
systems (LCs). The variety and profusion of attack vectors available to a
censor has led to an arms race, leading to a dramatic speed of evolution of
LCs. Despite their inherent complexity and the breadth of work in this area,
there is no systematic way to evaluate link circumvention systems and compare
them against each other. In this paper, we (i) sketch an attack model to
comprehensively explore a censor's capabilities, (ii) present an abstract model
of a LC, a system that helps a censored client communicate with a server over
the Internet while resisting censorship, (iii) describe an evaluation stack
that underscores a layered approach to evaluate LCs, and (iv) systemize and
evaluate existing censorship resistance systems that provide link
circumvention. We highlight open challenges in the evaluation and development
of LCs and discuss possible mitigations.Comment: Content from this paper was published in Proceedings on Privacy
Enhancing Technologies (PoPETS), Volume 2016, Issue 4 (July 2016) as "SoK:
Making Sense of Censorship Resistance Systems" by Sheharbano Khattak, Tariq
Elahi, Laurent Simon, Colleen M. Swanson, Steven J. Murdoch and Ian Goldberg
(DOI 10.1515/popets-2016-0028
Deep Reinforcement Learning for Resource Management in Network Slicing
Network slicing is born as an emerging business to operators, by allowing
them to sell the customized slices to various tenants at different prices. In
order to provide better-performing and cost-efficient services, network slicing
involves challenging technical issues and urgently looks forward to intelligent
innovations to make the resource management consistent with users' activities
per slice. In that regard, deep reinforcement learning (DRL), which focuses on
how to interact with the environment by trying alternative actions and
reinforcing the tendency actions producing more rewarding consequences, is
assumed to be a promising solution. In this paper, after briefly reviewing the
fundamental concepts of DRL, we investigate the application of DRL in solving
some typical resource management for network slicing scenarios, which include
radio resource slicing and priority-based core network slicing, and demonstrate
the advantage of DRL over several competing schemes through extensive
simulations. Finally, we also discuss the possible challenges to apply DRL in
network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201
Using Transcoding for Hidden Communication in IP Telephony
The paper presents a new steganographic method for IP telephony called
TranSteg (Transcoding Steganography). Typically, in steganographic
communication it is advised for covert data to be compressed in order to limit
its size. In TranSteg it is the overt data that is compressed to make space for
the steganogram. The main innovation of TranSteg is to, for a chosen voice
stream, find a codec that will result in a similar voice quality but smaller
voice payload size than the originally selected. Then, the voice stream is
transcoded. At this step the original voice payload size is intentionally
unaltered and the change of the codec is not indicated. Instead, after placing
the transcoded voice payload, the remaining free space is filled with hidden
data. TranSteg proof of concept implementation was designed and developed. The
obtained experimental results are enclosed in this paper. They prove that the
proposed method is feasible and offers a high steganographic bandwidth.
TranSteg detection is difficult to perform when performing inspection in a
single network localisation.Comment: 17 pages, 16 figures, 4 table
A Dynamic Macroeconomic Model for the US Telecommunications Industry
Dynamic models have been used in most businesses serving different purposes. The increased changes of the Telecommunications environment have created a dynamic industry emerging new dynamic economic models. We investigated the Telecom industry by conducting macroeconomic and infrastructure analysis. However, this paper uses recent data from the Telecommunications industry to reveal the infrastructure trends and predict the US wireless growth. The analysis is focused on several factors such as the infrastructure described by the Teledensity, the employment and the Telecom revenues in comparison with the Gross Domestic Product (GDP). The purpose of this analysis is to understand the industry’s behavior during a specific period of time, 1984-2003, propose an appropriate economic dynamic model, wireless oriented that identifies the current driving forces and detects the impact of some critical events and trends.Dynamic Economic Model, Macroeconomic Analysis, Telecom Act, Teledensity
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