6,692 research outputs found
Online Anomaly Detection with Sparse Gaussian Processes
Online anomaly detection of time-series data is an important and challenging
task in machine learning. Gaussian processes (GPs) are powerful and flexible
models for modeling time-series data. However, the high time complexity of GPs
limits their applications in online anomaly detection. Attributed to some
internal or external changes, concept drift usually occurs in time-series data,
where the characteristics of data and meanings of abnormal behaviors alter over
time. Online anomaly detection methods should have the ability to adapt to
concept drift. Motivated by the above facts, this paper proposes the method of
sparse Gaussian processes with Q-function (SGP-Q). The SGP-Q employs sparse
Gaussian processes (SGPs) whose time complexity is lower than that of GPs, thus
significantly speeding up online anomaly detection. By using Q-function
properly, the SGP-Q can adapt to concept drift well. Moreover, the SGP-Q makes
use of few abnormal data in the training data by its strategy of updating
training data, resulting in more accurate sparse Gaussian process regression
models and better anomaly detection results. We evaluate the SGP-Q on various
artificial and real-world datasets. Experimental results validate the
effectiveness of the SGP-Q
Statistical Structure Learning, Towards a Robust Smart Grid
Robust control and maintenance of the grid relies on accurate data. Both PMUs
and state estimators are prone to false data injection attacks. Thus, it is
crucial to have a mechanism for fast and accurate detection of an agent
maliciously tampering with the data---for both preventing attacks that may lead
to blackouts, and for routine monitoring and control tasks of current and
future grids. We propose a decentralized false data injection detection scheme
based on Markov graph of the bus phase angles. We utilize the Conditional
Covariance Test (CCT) to learn the structure of the grid. Using the DC power
flow model, we show that under normal circumstances, and because of
walk-summability of the grid graph, the Markov graph of the voltage angles can
be determined by the power grid graph. Therefore, a discrepancy between
calculated Markov graph and learned structure should trigger the alarm. Local
grid topology is available online from the protection system and we exploit it
to check for mismatch. Should a mismatch be detected, we use correlation
anomaly score to detect the set of attacked nodes. Our method can detect the
most recent stealthy deception attack on the power grid that assumes knowledge
of bus-branch model of the system and is capable of deceiving the state
estimator, damaging power network observatory, control, monitoring, demand
response and pricing schemes. Specifically, under the stealthy deception
attack, the Markov graph of phase angles changes. In addition to detect a state
of attack, our method can detect the set of attacked nodes. To the best of our
knowledge, our remedy is the first to comprehensively detect this sophisticated
attack and it does not need additional hardware. Moreover, our detection scheme
is successful no matter the size of the attacked subset. Simulation of various
power networks confirms our claims
AED-Net: An Abnormal Event Detection Network
It is challenging to detect the anomaly in crowded scenes for quite a long
time. In this paper, a self-supervised framework, abnormal event detection
network (AED-Net), which is composed of PCAnet and kernel principal component
analysis (kPCA), is proposed to address this problem. Using surveillance video
sequences of different scenes as raw data, PCAnet is trained to extract
high-level semantics of crowd's situation. Next, kPCA,a one-class classifier,
is trained to determine anomaly of the scene. In contrast to some prevailing
deep learning methods,the framework is completely self-supervised because it
utilizes only video sequences in a normal situation. Experiments of global and
local abnormal event detection are carried out on UMN and UCSD datasets, and
competitive results with higher EER and AUC compared to other state-of-the-art
methods are observed. Furthermore, by adding local response normalization (LRN)
layer, we propose an improvement to original AED-Net. And it is proved to
perform better by promoting the framework's generalization capacity according
to the experiments.Comment: 14 pages, 7 figure
Multi-Task Kernel Null-Space for One-Class Classification
The one-class kernel spectral regression (OC-KSR), the regression-based
formulation of the kernel null-space approach has been found to be an effective
Fisher criterion-based methodology for one-class classification (OCC),
achieving state-of-the-art performance in one-class classification while
providing relatively high robustness against data corruption. This work extends
the OC-KSR methodology to a multi-task setting where multiple one-class
problems share information for improved performance. By viewing the multi-task
structure learning problem as one of compositional function learning, first,
the OC-KSR method is extended to learn multiple tasks' structure
\textit{linearly} by posing it as an instantiation of the separable kernel
learning problem in a vector-valued reproducing kernel Hilbert space where an
output kernel encodes tasks' structure while another kernel captures input
similarities. Next, a non-linear structure learning mechanism is proposed which
captures multiple tasks' relationships \textit{non-linearly} via an output
kernel. The non-linear structure learning method is then extended to a sparse
setting where different tasks compete in an output composition mechanism,
leading to a sparse non-linear structure among multiple problems. Through
extensive experiments on different data sets, the merits of the proposed
multi-task kernel null-space techniques are verified against the baseline as
well as other existing multi-task one-class learning techniques
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
The widespread popularity of smart meters enables an immense amount of
fine-grained electricity consumption data to be collected. Meanwhile, the
deregulation of the power industry, particularly on the delivery side, has
continuously been moving forward worldwide. How to employ massive smart meter
data to promote and enhance the efficiency and sustainability of the power grid
is a pressing issue. To date, substantial works have been conducted on smart
meter data analytics. To provide a comprehensive overview of the current
research and to identify challenges for future research, this paper conducts an
application-oriented review of smart meter data analytics. Following the three
stages of analytics, namely, descriptive, predictive and prescriptive
analytics, we identify the key application areas as load analysis, load
forecasting, and load management. We also review the techniques and
methodologies adopted or developed to address each application. In addition, we
also discuss some research trends, such as big data issues, novel machine
learning technologies, new business models, the transition of energy systems,
and data privacy and security.Comment: IEEE Transactions on Smart Grid, 201
Baselining Network-Wide Traffic by Time-Frequency Constrained Stable Principal Component Pursuit
The Internet traffic analysis is important to network management,and
extracting the baseline traffic patterns is especially helpful for some
significant network applications.In this paper, we study on the baseline
problem of the traffic matrix satisfying a refined traffic matrix decomposition
model,since this model extends the assumption of the baseline traffic component
to characterize its smoothness, and is more realistic than the existing traffic
matrix models. We develop a novel baseline scheme, named Stable Principal
Component Pursuit with Time-Frequency Constraints (SPCP-TFC), which extends the
Stable Principal Component Pursuit (SPCP) by applying new time-frequency
constraints. Then we design an efficient numerical algorithm for SPCP-TFC. At
last, we evaluate this baseline scheme through simulations, and show it has
superior performance than the existing baseline schemes RBL and PCA.Comment: Accepted to AEU-International Journal of Electronics and
Communication
Robust One-Class Kernel Spectral Regression
The kernel null-space technique and its regression-based formulation (called
one-class kernel spectral regression, a.k.a. OC-KSR) is known to be an
effective and computationally attractive one-class classification framework.
Despite its outstanding performance, the applicability of kernel null-space
method is limited due to its susceptibility to possible training data
corruptions and inability to rank training observations according to their
conformity with the model. This work addresses these shortcomings by studying
the effect of regularising the solution of the null-space kernel Fisher
methodology in the context of its regression-based formulation (OC-KSR). In
this respect, first, the effect of a Tikhonov regularisation in the Hilbert
space is analysed where the one-class learning problem in presence of
contaminations in the training set is posed as a sensitivity analysis problem.
Next, driven by the success of the sparse representation methodology, the
effect of a sparsity regularisation on the solution is studied. For both
alternative regularisation schemes, iterative algorithms are proposed which
recursively update label confidences and rank training observations based on
their fit with the model. Through extensive experiments conducted on different
data sets, the proposed methodology is found to enhance robustness against
contamination in the training set as compared with the baseline kernel
null-space technique as well as other existing approaches in a one-class
classification paradigm while providing the functionality to rank training
samples effectively
Nearly second-order asymptotic optimality of sequential change-point detection with one-sample updates
Sequential change-point detection when the distribution parameters are
unknown is a fundamental problem in statistics and machine learning. When the
post-change parameters are unknown, we consider a set of detection procedures
based on sequential likelihood ratios with non-anticipating estimators
constructed using online convex optimization algorithms such as online mirror
descent, which provides a more versatile approach to tackle complex situations
where recursive maximum likelihood estimators cannot be found. When the
underlying distributions belong to a exponential family and the estimators
satisfy the logarithm regret property, we show that this approach is nearly
second-order asymptotically optimal. This means that the upper bound for the
false alarm rate of the algorithm (measured by the average-run-length) meets
the lower bound asymptotically up to a log-log factor when the threshold tends
to infinity. Our proof is achieved by making a connection between sequential
change-point and online convex optimization and leveraging the logarithmic
regret bound property of online mirror descent algorithm. Numerical and real
data examples validate our theory
Invisible Units Detection and Estimation Based on Random Matrix Theory
Invisible units mainly refer to small-scale units that are not monitored by,
and thus are not visible to utilities. Integration of these invisible units
into power systems does significantly affect the way in which a distribution
grid is planned and operated. This paper, based on random matrix theory (RMT),
proposes a statistical, data-driven framework to handle the massive grid data,
in contrast to its deterministic, model-based counterpart. Combining the
RMT-based data-mining framework with conventional techniques, some heuristics
are derived as the solution to the invisible units detection and estimation
task: linear eigenvalue statistic indicators (LESs) are suggested as the main
ingredients of the solution; according to the statistical properties of LESs,
the hypothesis testing is formulated to conduct change point detection in the
high-dimensional space. The proposed method is promising for anomaly detection
and pertinent to current distribution networks---it is capable of detecting
invisible power usage and fraudulent behavior while even being able to locate
the suspect's location. Case studies, using both simulated data and actual
data, validate the proposed method.Comment: 10 pages,Accepted by IEEE Transaction on Power System
Reduction of Monitoring Register on Software Defined Networks
Characterization of data network monitoring registers allows for reductions
in the number of data, which is essential when the information flow is high,
and implementation of processes with short response times, such as interchange
of control information between devices and anomaly detection is required. The
present investigation applied wavelet transforms, so as to characterize the
statistic monitoring register of a software-defined network. Its main
contribution lies in the obtention of a record that, although reduced, retains
detailed, essential information for the correct application of anomaly
detectors.Comment: 8 pages, 5 figure
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