A signal analysis of network traffic anomalies

By Paul R. Barford, Jeffery Kline, D. Plonka, Amos Ron. International Memory Workshop, 2002

1,013 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/f75e3be069b159baf1a3b2bd3aa9a7e06d03c227

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Citations in the corpus

Diagnosing network-wide traffic anomalies (2004, methodology): “A second class of methods process data in batches and are based on signal analysis techniques, such as the wavelet analysis scheme used in [1].”
A comprehensive survey on network anomaly detection (2018, background): “[16], anomalies are grouped into four categories: operational/misconfiguration/failure events; flash crowd/legitimate but abnormal use; measurement anomalies; and network abuse anomalies/malicious attacks (or simply, network attacks) [20,21].”
Characterization of network-wide anomalies in traffic flows (2004, methodology): “Barfordet al employed a wavelet-based signal analysis of flow traffic to ch aracterize single-link byte anomalies [3].”
Detecting and Preventing Cyber Insider Threats: A Survey (2018, background): “Thirdly, unusual or malicious user/host activities are detectable via connectivity analysis [99].”
Bohatei: Flexible and Elastic DDoS Defense (2015, methodology): “like capabilities, and custom code [27].”
A taxonomy of DDoS attack and DDoS defense mechanisms (2004, methodology): “Approaches presented in [74, 46, 38, 32, 23, 48, 4, 8, 9, 52, 51, 7] provide examples of anomaly detection approaches.”
Event labeling combining ensemble detectors and background knowledge (2014, background): “1b), all instances are checked individually by knowledge expert(s) and events are labeled manually [5].”
Preventing Internet denial-of-service with capabilities (2004, background): “Perhaps the most active area of DoS prevention work is anomaly detection [2] [6] [11].”
Sensitivity of PCA for traffic anomaly detection (2007, methodology): “Other statistical methods that have been used for traffic anomaly detection include Kalman filters [19], wavelets [4], among others.”
Combining filtering and statistical methods for anomaly detection (2005, methodology): “We explain here how any anomaly detection method can be viewed as a problem in statistical hypothesis testing.”
Structural analysis of network traffic flows (2004, methodology): “A number of change detection methods have be en proposed that rely on wavelet denoising techniques [1] and deviations from forecasted be havior [3, 13] to identify outliers.”
MAWILab: combining diverse anomaly detectors for automated anomaly labeling and performance benchmarking (2010, background): “Sometimes researchers construct ground truth data by manually inspecting the analyzed traffic [4].”