31,971 research outputs found
Towards Provably Invisible Network Flow Fingerprints
Network traffic analysis reveals important information even when messages are
encrypted. We consider active traffic analysis via flow fingerprinting by
invisibly embedding information into packet timings of flows. In particular,
assume Alice wishes to embed fingerprints into flows of a set of network input
links, whose packet timings are modeled by Poisson processes, without being
detected by a watchful adversary Willie. Bob, who receives the set of
fingerprinted flows after they pass through the network modeled as a collection
of independent and parallel queues, wishes to extract Alice's embedded
fingerprints to infer the connection between input and output links of the
network. We consider two scenarios: 1) Alice embeds fingerprints in all of the
flows; 2) Alice embeds fingerprints in each flow independently with probability
. Assuming that the flow rates are equal, we calculate the maximum number of
flows in which Alice can invisibly embed fingerprints while having those
fingerprints successfully decoded by Bob. Then, we extend the construction and
analysis to the case where flow rates are distinct, and discuss the extension
of the network model
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
A template-based sub-optimal content distribution for D2D content sharing networks
We propose Templatized Elastic Assignment (TEA), a light-weight scheme for mobile cooperative caching networks. It consists of two components, (1) one to calculate a sub-optimal distribution of each situation and (2) finegrained ID management by base stations (BSs) to achieve the calculated distribution. The former is modeled from findings that the desirable distribution plotted in a semilog graph forms a downward straight line with which the slope and Yintercept epend on the bias of request and total cache capacity, respectively. The latter is inspired from the identifier (ID)-based scheme, which ties devices and content by a randomly associated ID. TEA achieved the calculated distribution with IDs by using the annotation from base stations (BSs), which is preliminarily calculated by the template in a fine-grained density of devices. Moreover, such fine-grained management secondarily standardizes the cached content among multiple densities and enables the reuse of the content in devices from other BSs. Evaluation results indicate that our scheme reduces (1) 8.3 times more traffic than LFU and achieves almost the same amount of traffic reduction as with the genetic algorithm, (2) 45 hours of computation into a few seconds, and (3) at most 70% of content replacement across multiple BSs
The Internet of Things Connectivity Binge: What are the Implications?
Despite wide concern about cyberattacks, outages and privacy violations, most experts believe the Internet of Things will continue to expand successfully the next few years, tying machines to machines and linking people to valuable resources, services and opportunities
Dark clouds on the horizon:the challenge of cloud forensics
We introduce the challenges to digital forensics introduced by the advent and adoption of technologies, such as encryption, secure networking, secure processors and anonymous routing. All potentially render current approaches to digital forensic investigation unusable. We explain how the Cloud, due to its global distribution and multi-jurisdictional nature, exacerbates these challenges. The latest developments in the computing milieu threaten a complete “evidence blackout” with severe implications for the detection, investigation and prosecution of cybercrime. In this paper, we review the current landscape of cloud-based forensics investigations. We posit a number of potential solutions. Cloud forensic difficulties can only be addressed if we acknowledge its socio-technological nature, and design solutions that address both human and technological dimensions. No firm conclusion is drawn; rather the objective is to present a position paper, which will stimulate debate in the area and move the discipline of digital cloud forensics forward. Thus, the paper concludes with an invitation to further informed debate on this issue
Adaptive Traffic Fingerprinting for Darknet Threat Intelligence
Darknet technology such as Tor has been used by various threat actors for
organising illegal activities and data exfiltration. As such, there is a case
for organisations to block such traffic, or to try and identify when it is used
and for what purposes. However, anonymity in cyberspace has always been a
domain of conflicting interests. While it gives enough power to nefarious
actors to masquerade their illegal activities, it is also the cornerstone to
facilitate freedom of speech and privacy. We present a proof of concept for a
novel algorithm that could form the fundamental pillar of a darknet-capable
Cyber Threat Intelligence platform. The solution can reduce anonymity of users
of Tor, and considers the existing visibility of network traffic before
optionally initiating targeted or widespread BGP interception. In combination
with server HTTP response manipulation, the algorithm attempts to reduce the
candidate data set to eliminate client-side traffic that is most unlikely to be
responsible for server-side connections of interest. Our test results show that
MITM manipulated server responses lead to expected changes received by the Tor
client. Using simulation data generated by shadow, we show that the detection
scheme is effective with false positive rate of 0.001, while sensitivity
detecting non-targets was 0.016+-0.127. Our algorithm could assist
collaborating organisations willing to share their threat intelligence or
cooperate during investigations.Comment: 26 page
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