72,068 research outputs found
Detection of visitors in elderly care using a low-resolution visual sensor network
Loneliness is a common condition associated with aging and comes with extreme health consequences including decline in physical and mental health, increased mortality and poor living conditions. Detecting and assisting lonely persons is therefore important-especially in the home environment. The current studies analyse the Activities of Daily Living (ADL) usually with the focus on persons living alone, e.g., to detect health deterioration. However, this type of data analysis relies on the assumption of a single person being analysed, and the ADL data analysis becomes less reliable without assessing socialization in seniors for health state assessment and intervention. In this paper, we propose a network of cheap low-resolution visual sensors for the detection of visitors. The visitor analysis starts by visual feature extraction based on foreground/background detection and morphological operations to track the motion patterns in each visual sensor. Then, we utilize the features of the visual sensors to build a Hidden Markov Model (HMM) for the actual detection. Finally, a rule-based classifier is used to compute the number and the duration of visits. We evaluate our framework on a real-life dataset of ten months. The results show a promising visit detection performance when compared to ground truth
Detection of Early-Stage Enterprise Infection by Mining Large-Scale Log Data
Recent years have seen the rise of more sophisticated attacks including
advanced persistent threats (APTs) which pose severe risks to organizations and
governments by targeting confidential proprietary information. Additionally,
new malware strains are appearing at a higher rate than ever before. Since many
of these malware are designed to evade existing security products, traditional
defenses deployed by most enterprises today, e.g., anti-virus, firewalls,
intrusion detection systems, often fail at detecting infections at an early
stage.
We address the problem of detecting early-stage infection in an enterprise
setting by proposing a new framework based on belief propagation inspired from
graph theory. Belief propagation can be used either with "seeds" of compromised
hosts or malicious domains (provided by the enterprise security operation
center -- SOC) or without any seeds. In the latter case we develop a detector
of C&C communication particularly tailored to enterprises which can detect a
stealthy compromise of only a single host communicating with the C&C server.
We demonstrate that our techniques perform well on detecting enterprise
infections. We achieve high accuracy with low false detection and false
negative rates on two months of anonymized DNS logs released by Los Alamos
National Lab (LANL), which include APT infection attacks simulated by LANL
domain experts. We also apply our algorithms to 38TB of real-world web proxy
logs collected at the border of a large enterprise. Through careful manual
investigation in collaboration with the enterprise SOC, we show that our
techniques identified hundreds of malicious domains overlooked by
state-of-the-art security products
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
Most of the online news media outlets rely heavily on the revenues generated
from the clicks made by their readers, and due to the presence of numerous such
outlets, they need to compete with each other for reader attention. To attract
the readers to click on an article and subsequently visit the media site, the
outlets often come up with catchy headlines accompanying the article links,
which lure the readers to click on the link. Such headlines are known as
Clickbaits. While these baits may trick the readers into clicking, in the long
run, clickbaits usually don't live up to the expectation of the readers, and
leave them disappointed.
In this work, we attempt to automatically detect clickbaits and then build a
browser extension which warns the readers of different media sites about the
possibility of being baited by such headlines. The extension also offers each
reader an option to block clickbaits she doesn't want to see. Then, using such
reader choices, the extension automatically blocks similar clickbaits during
her future visits. We run extensive offline and online experiments across
multiple media sites and find that the proposed clickbait detection and the
personalized blocking approaches perform very well achieving 93% accuracy in
detecting and 89% accuracy in blocking clickbaits.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records
The pervasiveness and availability of mobile phone data offer the opportunity
of discovering usable knowledge about crowd behaviors in urban environments.
Cities can leverage such knowledge in order to provide better services (e.g.,
public transport planning, optimized resource allocation) and safer cities.
Call Detail Record (CDR) data represents a practical data source to detect and
monitor unusual events considering the high level of mobile phone penetration,
compared with GPS equipped and open devices. In this paper, we provide a
methodology that is able to detect unusual events from CDR data that typically
has low accuracy in terms of space and time resolution. Moreover, we introduce
a concept of unusual event that involves a large amount of people who expose an
unusual mobility behavior. Our careful consideration of the issues that come
from coarse-grained CDR data ultimately leads to a completely general framework
that can detect unusual crowd events from CDR data effectively and efficiently.
Through extensive experiments on real-world CDR data for a large city in
Africa, we demonstrate that our method can detect unusual events with 16%
higher recall and over 10 times higher precision, compared to state-of-the-art
methods. We implement a visual analytics prototype system to help end users
analyze detected unusual crowd events to best suit different application
scenarios. To the best of our knowledge, this is the first work on the
detection of unusual events from CDR data with considerations of its temporal
and spatial sparseness and distinction between user unusual activities and
daily routines.Comment: 18 pages, 6 figure
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