18,586 research outputs found
Structural Analysis of Network Traffic Matrix via Relaxed Principal Component Pursuit
The network traffic matrix is widely used in network operation and
management. It is therefore of crucial importance to analyze the components and
the structure of the network traffic matrix, for which several mathematical
approaches such as Principal Component Analysis (PCA) were proposed. In this
paper, we first argue that PCA performs poorly for analyzing traffic matrix
that is polluted by large volume anomalies, and then propose a new
decomposition model for the network traffic matrix. According to this model, we
carry out the structural analysis by decomposing the network traffic matrix
into three sub-matrices, namely, the deterministic traffic, the anomaly traffic
and the noise traffic matrix, which is similar to the Robust Principal
Component Analysis (RPCA) problem previously studied in [13]. Based on the
Relaxed Principal Component Pursuit (Relaxed PCP) method and the Accelerated
Proximal Gradient (APG) algorithm, we present an iterative approach for
decomposing a traffic matrix, and demonstrate its efficiency and flexibility by
experimental results. Finally, we further discuss several features of the
deterministic and noise traffic. Our study develops a novel method for the
problem of structural analysis of the traffic matrix, which is robust against
pollution of large volume anomalies.Comment: Accepted to Elsevier Computer Network
SENATUS: An Approach to Joint Traffic Anomaly Detection and Root Cause Analysis
In this paper, we propose a novel approach, called SENATUS, for joint traffic
anomaly detection and root-cause analysis. Inspired from the concept of a
senate, the key idea of the proposed approach is divided into three stages:
election, voting and decision. At the election stage, a small number of
\nop{traffic flow sets (termed as senator flows)}senator flows are chosen\nop{,
which are used} to represent approximately the total (usually huge) set of
traffic flows. In the voting stage, anomaly detection is applied on the senator
flows and the detected anomalies are correlated to identify the most possible
anomalous time bins. Finally in the decision stage, a machine learning
technique is applied to the senator flows of each anomalous time bin to find
the root cause of the anomalies. We evaluate SENATUS using traffic traces
collected from the Pan European network, GEANT, and compare against another
approach which detects anomalies using lossless compression of traffic
histograms. We show the effectiveness of SENATUS in diagnosing anomaly types:
network scans and DoS/DDoS attacks
Robust Linear Spectral Unmixing using Anomaly Detection
This paper presents a Bayesian algorithm for linear spectral unmixing of
hyperspectral images that accounts for anomalies present in the data. The model
proposed assumes that the pixel reflectances are linear mixtures of unknown
endmembers, corrupted by an additional nonlinear term modelling anomalies and
additive Gaussian noise. A Markov random field is used for anomaly detection
based on the spatial and spectral structures of the anomalies. This allows
outliers to be identified in particular regions and wavelengths of the data
cube. A Bayesian algorithm is proposed to estimate the parameters involved in
the model yielding a joint linear unmixing and anomaly detection algorithm.
Simulations conducted with synthetic and real hyperspectral images demonstrate
the accuracy of the proposed unmixing and outlier detection strategy for the
analysis of hyperspectral images
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series
Decomposing complex time series into trend, seasonality, and remainder
components is an important task to facilitate time series anomaly detection and
forecasting. Although numerous methods have been proposed, there are still many
time series characteristics exhibiting in real-world data which are not
addressed properly, including 1) ability to handle seasonality fluctuation and
shift, and abrupt change in trend and reminder; 2) robustness on data with
anomalies; 3) applicability on time series with long seasonality period. In the
paper, we propose a novel and generic time series decomposition algorithm to
address these challenges. Specifically, we extract the trend component robustly
by solving a regression problem using the least absolute deviations loss with
sparse regularization. Based on the extracted trend, we apply the the non-local
seasonal filtering to extract the seasonality component. This process is
repeated until accurate decomposition is obtained. Experiments on different
synthetic and real-world time series datasets demonstrate that our method
outperforms existing solutions.Comment: Accepted to the thirty-third AAAI Conference on Artificial
Intelligence (AAAI 2019), 9 pages, 5 figure
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