36,411 research outputs found
Properties of Microlensing Light Curve Anomalies Induced by Multiple Planets
In this paper, we show that the pattern of microlensing light curve anomalies
induced by multiple planets are well described by the superposition of those of
the single-planet systems where the individual planet-primary binary pairs act
as independent lens systems. Since the outer deviation regions around the
planetary caustics of the individual planets occur in general at different
locations, we find that the pattern of anomalies in these regions are hardly
affected by the existence of other planet(s). This implies that even if an
event is caused by a multiple planetary system, a simple single-planet lensing
model is good enough for the description of most anomalies caused by the source
passage of the outer deviation regions. Detection of the anomalies resulting
from the source trajectory passing both the outer deviation regions caused by
more than two planets will provide a new channel of detecting multiple planets.Comment: total 13 pages, including 6 figures and no table, MNRAS, submitted,
for better quality pdf file is avalilable at
http://astroph.chungbuk.ac.kr/~cheongho/publication.htm
A Machine-Synesthetic Approach To DDoS Network Attack Detection
In the authors' opinion, anomaly detection systems, or ADS, seem to be the
most perspective direction in the subject of attack detection, because these
systems can detect, among others, the unknown (zero-day) attacks. To detect
anomalies, the authors propose to use machine synesthesia. In this case,
machine synesthesia is understood as an interface that allows using image
classification algorithms in the problem of detecting network anomalies, making
it possible to use non-specialized image detection methods that have recently
been widely and actively developed. The proposed approach is that the network
traffic data is "projected" into the image. It can be seen from the
experimental results that the proposed method for detecting anomalies shows
high results in the detection of attacks. On a large sample, the value of the
complex efficiency indicator reaches 97%.Comment: 12 pages, 2 figures, 5 tables. Accepted to the Intelligent Systems
Conference (IntelliSys) 201
HYPA: Efficient Detection of Path Anomalies in Time Series Data on Networks
The unsupervised detection of anomalies in time series data has important
applications in user behavioral modeling, fraud detection, and cybersecurity.
Anomaly detection has, in fact, been extensively studied in categorical
sequences. However, we often have access to time series data that represent
paths through networks. Examples include transaction sequences in financial
networks, click streams of users in networks of cross-referenced documents, or
travel itineraries in transportation networks. To reliably detect anomalies, we
must account for the fact that such data contain a large number of independent
observations of paths constrained by a graph topology. Moreover, the
heterogeneity of real systems rules out frequency-based anomaly detection
techniques, which do not account for highly skewed edge and degree statistics.
To address this problem, we introduce HYPA, a novel framework for the
unsupervised detection of anomalies in large corpora of variable-length
temporal paths in a graph. HYPA provides an efficient analytical method to
detect paths with anomalous frequencies that result from nodes being traversed
in unexpected chronological order.Comment: 11 pages with 8 figures and supplementary material. To appear at SIAM
Data Mining (SDM 2020
Contextual anomaly detection in crowded surveillance scenes
AbstractThis work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour
Interpretable Aircraft Engine Diagnostic via Expert Indicator Aggregation
Detecting early signs of failures (anomalies) in complex systems is one of
the main goal of preventive maintenance. It allows in particular to avoid
actual failures by (re)scheduling maintenance operations in a way that
optimizes maintenance costs. Aircraft engine health monitoring is one
representative example of a field in which anomaly detection is crucial.
Manufacturers collect large amount of engine related data during flights which
are used, among other applications, to detect anomalies. This article
introduces and studies a generic methodology that allows one to build automatic
early signs of anomaly detection in a way that builds upon human expertise and
that remains understandable by human operators who make the final maintenance
decision. The main idea of the method is to generate a very large number of
binary indicators based on parametric anomaly scores designed by experts,
complemented by simple aggregations of those scores. A feature selection method
is used to keep only the most discriminant indicators which are used as inputs
of a Naive Bayes classifier. This give an interpretable classifier based on
interpretable anomaly detectors whose parameters have been optimized indirectly
by the selection process. The proposed methodology is evaluated on simulated
data designed to reproduce some of the anomaly types observed in real world
engines.Comment: arXiv admin note: substantial text overlap with arXiv:1408.6214,
arXiv:1409.4747, arXiv:1407.088
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