Corpus ID: 16672252

Network Traffic Decomposition for Anomaly Detection

@article{Babaie2014NetworkTD,
  title={Network Traffic Decomposition for Anomaly Detection},
  author={Tahereh Babaie and Sanjay Chawla and Sebastien Ardon},
  journal={ArXiv},
  year={2014},
  volume={abs/1403.0157}
}
In this paper we focus on the detection of network anomalies like Denial of Service (DoS) attacks and port scans in a unified manner. While there has been an extensive amount of research in network anomaly detection, current state of the art methods are only able to detect one class of anomalies at the cost of others. The key tool we will use is based on the spectral decomposition of a trajectory/hankel matrix which is able to detect deviations from both between and within correlation present… Expand
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References

SHOWING 1-10 OF 54 REFERENCES
Sensitivity of PCA for traffic anomaly detection
TLDR
This study identifies and evaluates four main challenges of using PCA to detect traffic anomalies: the false positive rate is very sensitive to small differences in the number of principal components in the normal subspace, the effectiveness of PCA is sensitive to the level of aggregation of the traffic measurements, a large anomaly may in advertently pollute the normalSubspace. Expand
Characterization of network-wide anomalies in traffic flows
TLDR
This paper presents the first large-scale exploration of the power of the subspace method when applied to flow traffic, and finds that almost all of the anomalies detected represent events of interest to network operators. Expand
A signal analysis of network traffic anomalies
TLDR
This paper reports results of signal analysis of four classes of network traffic anomalies: outages, flash crowds, attacks and measurement failures, and shows that wavelet filters are quite effective at exposing the details of both ambient and anomalous traffic. Expand
Diagnosing network-wide traffic anomalies
TLDR
A general method based on a separation of the high-dimensional space occupied by a set of network traffic measurements into disjoint subspaces corresponding to normal and anomalous network conditions to diagnose anomalies is proposed. Expand
ASTUTE: detecting a different class of traffic anomalies
TLDR
This work designs a computationally simple detection method for correlated anomalous flows, and discovers that this method uncovers a different class of anomalies than previous techniques do. Expand
Combining filtering and statistical methods for anomaly detection
TLDR
It is explained here how any anomaly detection method can be viewed as a problem in statistical hypothesis testing, and four different methods for analyzing residuals, two of which are new are studied and compared. Expand
Network anomography
TLDR
A new dynamic anomography algorithm is introduced, which effectively tracks routing and traffic change, so as to alert with high fidelity on intrinsic changes in network-level traffic, yet not on internal routing changes, an additional benefit of dynamicanomography is that it is robust to missing data, an important operational reality. Expand
The need for simulation in evaluating anomaly detectors
TLDR
This paper argues that there are numerous important questions regarding the effectiveness of anomaly detectors that cannot be answered by the evaluation techniques employed today and presents an outline of an evaluation methodology that leverages both simulation and traces from operational networks. Expand
Applying PCA for Traffic Anomaly Detection: Problems and Solutions
TLDR
A slightly modified version of PCA is developed that uses only data from a single router and proposes a solution to deal with the main problem, that PCA fails to capture temporal correlation, and is replaced with the Karhunen-Loeve transform. Expand
URCA: Pulling out Anomalies by their Root Causes
TLDR
This work introduces Unsupervised Root Cause Analysis (URCA) which isolates anomalous traffic and classifies alarms with minimal manual assistance and high accuracy, and shows that URCA can accurately diagnose a large range of anomaly types, including network scans, DDoS attacks, and major routing changes. Expand
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