1,672 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
A Review on Features’ Robustness in High Diversity Mobile Traffic Classifications
Mobile traffics are becoming more dominant due to growing usage of mobile devices and proliferation of IoT. The influx of mobile traffics introduce some new challenges in traffic classifications; namely the diversity complexity and behavioral dynamism complexity. Existing traffic classifications methods are designed for classifying standard protocols and user applications with more deterministic behaviors in small diversity. Currently, flow statistics, payload signature and heuristic traffic attributes are some of the most effective features used to discriminate traffic classes. In this paper, we investigate the correlations of these features to the less-deterministic user application traffic classes based on corresponding classification accuracy. Then, we evaluate the impact of large-scale classification on feature's robustness based on sign of diminishing accuracy. Our experimental results consolidate the needs for unsupervised feature learning to address the dynamism of mobile application behavioral traits for accurate classification on rapidly growing mobile traffics
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks
A Survey on Acoustic Side Channel Attacks on Keyboards
Most electronic devices utilize mechanical keyboards to receive inputs,
including sensitive information such as authentication credentials, personal
and private data, emails, plans, etc. However, these systems are susceptible to
acoustic side-channel attacks. Researchers have successfully developed methods
that can extract typed keystrokes from ambient noise. As the prevalence of
keyboard-based input systems continues to expand across various computing
platforms, and with the improvement of microphone technology, the potential
vulnerability to acoustic side-channel attacks also increases. This survey
paper thoroughly reviews existing research, explaining why such attacks are
feasible, the applicable threat models, and the methodologies employed to
launch and enhance these attacks.Comment: 22 pages, conferenc
Multi-view Multi-label Anomaly Network Traffic Classification based on MLP-Mixer Neural Network
Network traffic classification is the basis of many network security
applications and has attracted enough attention in the field of cyberspace
security. Existing network traffic classification based on convolutional neural
networks (CNNs) often emphasizes local patterns of traffic data while ignoring
global information associations. In this paper, we propose a MLP-Mixer based
multi-view multi-label neural network for network traffic classification.
Compared with the existing CNN-based methods, our method adopts the MLP-Mixer
structure, which is more in line with the structure of the packet than the
conventional convolution operation. In our method, the packet is divided into
the packet header and the packet body, together with the flow features of the
packet as input from different views. We utilize a multi-label setting to learn
different scenarios simultaneously to improve the classification performance by
exploiting the correlations between different scenarios. Taking advantage of
the above characteristics, we propose an end-to-end network traffic
classification method. We conduct experiments on three public datasets, and the
experimental results show that our method can achieve superior performance.Comment: 15 pages,6 figure
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