Combining filtering and statistical methods for anomaly detection
By Augustin Soule, Kave Salamatian, Nina Taft. ACM/SIGCOMM Internet Measurement Conference, 2005
368 citations recorded by the source corpus.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/0195d819d26355e714007d8a4bce04d0700312ed
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In Artificial Intelligence
- Blaine Nelson
- Battista Biggio
- Fabio Roli
- Benjamin I. P. Rubinstein
- Giorgio Fumera
- Anthony D. Joseph
- Ling Huang
- J. Doug Tygar
Citations in the corpus
- Compromising PCA-based Anomaly Detectors for Network-Wide Traffic (2008, methodology): “In previous usage scenarios [2, 5], the PCA detector is retrained regularly (e.”
- ANTIDOTE: understanding and defending against poisoning of anomaly detectors (2009, background): “…D. Joseph1,2 Shing-hon Lau1 Satish Rao1 Nina Taft2 J. D. Tygar1 1Computer Science Division, University of California, Berkeley 2Intel Labs Berkeley ABSTRACT Statistical machine learning techniques have recently garnered increased popularity as a means to improve network design and security.”
- 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.”
- Network Anomaly Detection: Methods, Systems and Tools (2014, background): “Techniques operating in supervised or semi-supervised modes [204] can be applied to detect bulk anomalies.”
- Anomaly detection: A survey (2009, neutral): “Techniques operating in supervised or semisupervised modes can be applied to detect bulk anomalies [Sun et al. 2007; Soule et al. 2005].”
- Behavior of Machine Learning Algorithms in Adversarial Environments (2010, background): “Targeted Tan et al. [2002], Lowd and Meek [2005a], Wittel and Wu [2004], Lowd and Meek [2005b] Moore et al.”
- A Survey on Internet Traffic Identification (2009, methodology): “While focusing on anomaly detection (not application iden-ti fi cation), Soule et al [66] use Origin-Destination fl ows to generate a traf fi c matrix and compare four tests (the last two new proposals) to detect anomalies, namely, threshold, deviation score, wavelet transform and generalized…”