12,491 research outputs found
Feature Extraction from Degree Distribution for Comparison and Analysis of Complex Networks
The degree distribution is an important characteristic of complex networks.
In many data analysis applications, the networks should be represented as
fixed-length feature vectors and therefore the feature extraction from the
degree distribution is a necessary step. Moreover, many applications need a
similarity function for comparison of complex networks based on their degree
distributions. Such a similarity measure has many applications including
classification and clustering of network instances, evaluation of network
sampling methods, anomaly detection, and study of epidemic dynamics. The
existing methods are unable to effectively capture the similarity of degree
distributions, particularly when the corresponding networks have different
sizes. Based on our observations about the structure of the degree
distributions in networks over time, we propose a feature extraction and a
similarity function for the degree distributions in complex networks. We
propose to calculate the feature values based on the mean and standard
deviation of the node degrees in order to decrease the effect of the network
size on the extracted features. The proposed method is evaluated using
different artificial and real network datasets, and it outperforms the state of
the art methods with respect to the accuracy of the distance function and the
effectiveness of the extracted features.Comment: arXiv admin note: substantial text overlap with arXiv:1307.362
Locality statistics for anomaly detection in time series of graphs
The ability to detect change-points in a dynamic network or a time series of
graphs is an increasingly important task in many applications of the emerging
discipline of graph signal processing. This paper formulates change-point
detection as a hypothesis testing problem in terms of a generative latent
position model, focusing on the special case of the Stochastic Block Model time
series. We analyze two classes of scan statistics, based on distinct underlying
locality statistics presented in the literature. Our main contribution is the
derivation of the limiting distributions and power characteristics of the
competing scan statistics. Performance is compared theoretically, on synthetic
data, and on the Enron email corpus. We demonstrate that both statistics are
admissible in one simple setting, while one of the statistics is inadmissible a
second setting.Comment: 15 pages, 6 figure
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
Quantification and Comparison of Degree Distributions in Complex Networks
The degree distribution is an important characteristic of complex networks.
In many applications, quantification of degree distribution in the form of a
fixed-length feature vector is a necessary step. On the other hand, we often
need to compare the degree distribution of two given networks and extract the
amount of similarity between the two distributions. In this paper, we propose a
novel method for quantification of the degree distributions in complex
networks. Based on this quantification method,a new distance function is also
proposed for degree distributions, which captures the differences in the
overall structure of the two given distributions. The proposed method is able
to effectively compare networks even with different scales, and outperforms the
state of the art methods considerably, with respect to the accuracy of the
distance function
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