2,925 research outputs found
On the Reverse Engineering of the Citadel Botnet
Citadel is an advanced information-stealing malware which targets financial
information. This malware poses a real threat against the confidentiality and
integrity of personal and business data. A joint operation was recently
conducted by the FBI and the Microsoft Digital Crimes Unit in order to take
down Citadel command-and-control servers. The operation caused some disruption
in the botnet but has not stopped it completely. Due to the complex structure
and advanced anti-reverse engineering techniques, the Citadel malware analysis
process is both challenging and time-consuming. This allows cyber criminals to
carry on with their attacks while the analysis is still in progress. In this
paper, we present the results of the Citadel reverse engineering and provide
additional insight into the functionality, inner workings, and open source
components of the malware. In order to accelerate the reverse engineering
process, we propose a clone-based analysis methodology. Citadel is an offspring
of a previously analyzed malware called Zeus; thus, using the former as a
reference, we can measure and quantify the similarities and differences of the
new variant. Two types of code analysis techniques are provided in the
methodology, namely assembly to source code matching and binary clone
detection. The methodology can help reduce the number of functions requiring
manual analysis. The analysis results prove that the approach is promising in
Citadel malware analysis. Furthermore, the same approach is applicable to
similar malware analysis scenarios.Comment: 10 pages, 17 figures. This is an updated / edited version of a paper
appeared in FPS 201
Correlating Pedestrian Flows and Search Engine Queries
An important challenge for ubiquitous computing is the development of
techniques that can characterize a location vis-a-vis the richness and
diversity of urban settings. In this paper we report our work on correlating
urban pedestrian flows with Google search queries. Using longitudinal data we
show pedestrian flows at particular locations can be correlated with the
frequency of Google search terms that are semantically relevant to those
locations. Our approach can identify relevant content, media, and
advertisements for particular locations.Comment: 4 pages, 1 figure, 1 tabl
Are we teaching our students what they need to know about ageing? Results from the National Survey of Undergraduate Teaching in Ageing and Geriatric Medicine
Introduction - Learning about ageing and the appropriate management of older patients is important for all doctors. This survey set out to evaluate what medical undergraduates in the UK are taught about ageing and geriatric medicine and how this teaching is delivered.
Methods – An electronic questionnaire was developed and sent to the 28/31 UK medical schools which agreed to participate.
Results – Full responses were received from 17 schools. 8/21 learning objectives were recorded as taught, and none were examined, across every school surveyed. Elder abuse and terminology and classification of health were taught in only 8/17 and 2/17 schools respectively. Pressure ulcers were taught about in 14/17 schools but taught formally in only 7 of these and examined in only 9. With regard to bio- and socio- gerontology, only 9/17 schools reported teaching in social ageing, 7/17 in cellular ageing and 9/17 in the physiology of ageing.
Discussion – Even allowing for the suboptimal response rate, this study presents significant cause for concern with UK undergraduate education related to ageing. The failure to teach comprehensively on elder abuse and pressure sores, in particular, may be significantly to the detriment of older patients
Mobile Communication Signatures of Unemployment
The mapping of populations socio-economic well-being is highly constrained by
the logistics of censuses and surveys. Consequently, spatially detailed changes
across scales of days, weeks, or months, or even year to year, are difficult to
assess; thus the speed of which policies can be designed and evaluated is
limited. However, recent studies have shown the value of mobile phone data as
an enabling methodology for demographic modeling and measurement. In this work,
we investigate whether indicators extracted from mobile phone usage can reveal
information about the socio-economical status of microregions such as districts
(i.e., average spatial resolution < 2.7km). For this we examine anonymized
mobile phone metadata combined with beneficiaries records from unemployment
benefit program. We find that aggregated activity, social, and mobility
patterns strongly correlate with unemployment. Furthermore, we construct a
simple model to produce accurate reconstruction of district level unemployment
from their mobile communication patterns alone. Our results suggest that
reliable and cost-effective economical indicators could be built based on
passively collected and anonymized mobile phone data. With similar data being
collected every day by telecommunication services across the world,
survey-based methods of measuring community socioeconomic status could
potentially be augmented or replaced by such passive sensing methods in the
future
Gamma-ray emission revealed at the western edge of SNR G344.7-0.1
We report on the investigation of a very high energy (VHE), Galactic
gamma-ray source recently discovered at >50GeV using the Large Area Telescope
(LAT) on board the Fermi Gamma-Ray Space Telescope. This object, 2FHL
J1703.4-4145, displays a very hard >50GeV spectrum with a photon index ~1.2 in
the 2FHL catalog and, as such, is one of the most extreme sources in the 2FHL
sub-sample of Galactic objects. A detailed analysis of the available
multi-wavelength data shows that this source is located on the western edge of
the supernova remnant (SNR) G344.7--0.1, along with extended TeV source, HESS
J1702-420. The observations and the spectral energy distribution modeling
support a scenario where this gamma-ray source is the byproduct of the
interaction between the SNR shock and the dense surrounding medium, with
escaping cosmic rays (CRs) diffusing into the dense environment and interacting
with a large local cloud, generating the observed TeV emission. If confirmed,
an interaction between the SNR CRs and a nearby cloud would make 2FHL
J1703.4-4145 another promising candidate for efficient particle acceleration of
the 2FHL Galactic sample, following the first candidate from our previous
investigation of a likely shock-cloud interaction occurring on the West edge of
the Vela SNR.Comment: 9 pages, 7 figures, Submitted to ApJ June 15, 2020. Accepted for
publication Oct 2, 202
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Detecting Community Structure in Dynamic Social Networks Using the Concept of Leadership
Detecting community structure in social networks is a fundamental problem
empowering us to identify groups of actors with similar interests. There have
been extensive works focusing on finding communities in static networks,
however, in reality, due to dynamic nature of social networks, they are
evolving continuously. Ignoring the dynamic aspect of social networks, neither
allows us to capture evolutionary behavior of the network nor to predict the
future status of individuals. Aside from being dynamic, another significant
characteristic of real-world social networks is the presence of leaders, i.e.
nodes with high degree centrality having a high attraction to absorb other
members and hence to form a local community. In this paper, we devised an
efficient method to incrementally detect communities in highly dynamic social
networks using the intuitive idea of importance and persistence of community
leaders over time. Our proposed method is able to find new communities based on
the previous structure of the network without recomputing them from scratch.
This unique feature, enables us to efficiently detect and track communities
over time rapidly. Experimental results on the synthetic and real-world social
networks demonstrate that our method is both effective and efficient in
discovering communities in dynamic social networks
From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles
The inference of network topologies from relational data is an important
problem in data analysis. Exemplary applications include the reconstruction of
social ties from data on human interactions, the inference of gene
co-expression networks from DNA microarray data, or the learning of semantic
relationships based on co-occurrences of words in documents. Solving these
problems requires techniques to infer significant links in noisy relational
data. In this short paper, we propose a new statistical modeling framework to
address this challenge. It builds on generalized hypergeometric ensembles, a
class of generative stochastic models that give rise to analytically tractable
probability spaces of directed, multi-edge graphs. We show how this framework
can be used to assess the significance of links in noisy relational data. We
illustrate our method in two data sets capturing spatio-temporal proximity
relations between actors in a social system. The results show that our
analytical framework provides a new approach to infer significant links from
relational data, with interesting perspectives for the mining of data on social
systems.Comment: 10 pages, 8 figures, accepted at SocInfo201
Revisiting \u3csup\u3e228\u3c/sup\u3eTh as a Tool for Determining Sedimentation and Mass Accumulation Rates
The use of 228Th has seen limited application for determining sedimentation and mass accumulation rates in coastal and marine environments. Recent analytical advances have enabled rapid, precise measurements of particle-bound 228Th using a radium delayed coincidence counting system (RaDeCC). Herein we review the 228Th cycle in the marine environment and revisit the historical use of 228Th as a tracer for determining sediment vertical accretion and mass accumulation rates in light of new measurement techniques. Case studies comparing accumulation rates from 228Th and 210Pb are presented for a micro-tidal salt marsh and a marginal sea environment. 228Th and 210Pb have been previously measured in mangrove, deltaic, continental shelf and ocean basin environments, and a literature synthesis reveals that 228Th (measured via alpha or gamma spectrometry) derived accumulation rates are generally equal to or greater than estimates derived from 210Pb, reflecting different integration periods. Use of 228Th is well-suited for shallow (\u3c15 cm) cores over decadal timescales. Application is limited to relatively homogenous sediment profiles with minor variations in grain size and minimal bioturbation. When appropriate conditions are met, complimentary use of 228Th and 210Pb can demonstrate that the upper layers of a core are undisturbed and can improve spatial coverage in mapping accumulation rates due to the higher sample throughput for sediment 228Th
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