2,587 research outputs found
Unified Description for Network Information Hiding Methods
Until now hiding methods in network steganography have been described in
arbitrary ways, making them difficult to compare. For instance, some
publications describe classical channel characteristics, such as robustness and
bandwidth, while others describe the embedding of hidden information. We
introduce the first unified description of hiding methods in network
steganography. Our description method is based on a comprehensive analysis of
the existing publications in the domain. When our description method is applied
by the research community, future publications will be easier to categorize,
compare and extend. Our method can also serve as a basis to evaluate the
novelty of hiding methods proposed in the future.Comment: 24 pages, 7 figures, 1 table; currently under revie
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
Hidden and Uncontrolled - On the Emergence of Network Steganographic Threats
Network steganography is the art of hiding secret information within innocent
network transmissions. Recent findings indicate that novel malware is
increasingly using network steganography. Similarly, other malicious activities
can profit from network steganography, such as data leakage or the exchange of
pedophile data. This paper provides an introduction to network steganography
and highlights its potential application for harmful purposes. We discuss the
issues related to countering network steganography in practice and provide an
outlook on further research directions and problems.Comment: 11 page
xLED: Covert Data Exfiltration from Air-Gapped Networks via Router LEDs
In this paper we show how attackers can covertly leak data (e.g., encryption
keys, passwords and files) from highly secure or air-gapped networks via the
row of status LEDs that exists in networking equipment such as LAN switches and
routers. Although it is known that some network equipment emanates optical
signals correlated with the information being processed by the device
('side-channel'), intentionally controlling the status LEDs to carry any type
of data ('covert-channel') has never studied before. A malicious code is
executed on the LAN switch or router, allowing full control of the status LEDs.
Sensitive data can be encoded and modulated over the blinking of the LEDs. The
generated signals can then be recorded by various types of remote cameras and
optical sensors. We provide the technical background on the internal
architecture of switches and routers (at both the hardware and software level)
which enables this type of attack. We also present amplitude and frequency
based modulation and encoding schemas, along with a simple transmission
protocol. We implement a prototype of an exfiltration malware and discuss its
design and implementation. We evaluate this method with a few routers and
different types of LEDs. In addition, we tested various receivers including
remote cameras, security cameras, smartphone cameras, and optical sensors, and
also discuss different detection and prevention countermeasures. Our experiment
shows that sensitive data can be covertly leaked via the status LEDs of
switches and routers at a bit rates of 10 bit/sec to more than 1Kbit/sec per
LED
SnapCatch: Automatic Detection of Covert Timing Channels Using Image Processing and Machine Learning
With the rapid growth of data exfiltration carried out by cyber attacks, Covert Timing Channels (CTC) have become an imminent network security risk that continues to grow in both sophistication and utilization. These types of channels utilize inter-arrival times to steal sensitive data from the targeted networks. CTC detection relies increasingly on machine learning techniques, which utilize statistical-based metrics to separate malicious (covert) traffic flows from the legitimate (overt) ones. However, given the efforts of cyber attacks to evade detection and the growing column of CTC, covert channels detection needs to improve in both performance and precision to detect and prevent CTCs and mitigate the reduction of the quality of service caused by the detection process. In this article, we present an innovative image-based solution for fully automated CTC detection and localization. Our approach is based on the observation that the covert channels generate traffic that can be converted to colored images. Leveraging this observation, our solution is designed to automatically detect and locate the malicious part (i.e., set of packets) within a traffic flow. By locating the covert parts within traffic flows, our approach reduces the drop of the quality of service caused by blocking the entire traffic flows in which covert channels are detected. We first convert traffic flows into colored images, and then we extract image-based features for detection covert traffic. We train a classifier using these features on a large data set of covert and overt traffic. This approach demonstrates a remarkable performance achieving a detection accuracy of 95.83% for cautious CTCs and a covert traffic accuracy of 97.83% for 8 bit covert messages, which is way beyond what the popular statistical-based solutions can achieve
Outsmarting Network Security with SDN Teleportation
Software-defined networking is considered a promising new paradigm, enabling
more reliable and formally verifiable communication networks. However, this
paper shows that the separation of the control plane from the data plane, which
lies at the heart of Software-Defined Networks (SDNs), introduces a new
vulnerability which we call \emph{teleportation}. An attacker (e.g., a
malicious switch in the data plane or a host connected to the network) can use
teleportation to transmit information via the control plane and bypass critical
network functions in the data plane (e.g., a firewall), and to violate security
policies as well as logical and even physical separations. This paper
characterizes the design space for teleportation attacks theoretically, and
then identifies four different teleportation techniques. We demonstrate and
discuss how these techniques can be exploited for different attacks (e.g.,
exfiltrating confidential data at high rates), and also initiate the discussion
of possible countermeasures. Generally, and given today's trend toward more
intent-based networking, we believe that our findings are relevant beyond the
use cases considered in this paper.Comment: Accepted in EuroSP'1
A Deep Learning Based Approach To Detect Covert Channels Attacks and Anomaly In New Generation Internet Protocol IPv6
The increased dependence of internet-based technologies in all facets of life
challenges the government and policymakers with the need for effective shield mechanism
against passive and active violations. Following up with the Qatar national vision 2030
activities and its goals for “Achieving Security, stability and maintaining public safety”
objectives, the present paper aims to propose a model for safeguarding the information and
monitor internet communications effectively. The current study utilizes a deep learning
based approach for detecting malicious communications in the network traffic. Considering
the efficiency of deep learning in data analysis and classification, a convolutional neural
network model was proposed. The suggested model is equipped for detecting attacks in
IPv6. The performance of the proposed detection algorithm was validated using a number
of datasets, including a newly created dataset. The performance of the model was evaluated
for covert channel, DDoS attacks detection in IPv6 and for anomaly detection. The
performance assessment produced an accuracy of 100%, 85% and 98% for covert channel
detection, DDoS detection and anomaly detection respectively. The project put forward a
novel approach for detecting suspicious communications in the network traffic
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