Robust Anomaly Detection Using Support Vector Machines

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Anomaly detection: A survey (2009, unknown): “RSVM have been applied to system call intrusion detection [Hu et al. 2003].”
Enhancing one-class support vector machines for unsupervised anomaly detection (2013, background): “Examples include handwritten digit recognition [50], object recognition [44], speaker identification [11], text categorization [10] and anomaly detection [30].”
A survey of network anomaly detection techniques (2016, methodology): “Interestingly, in a paper by Hu et al. (2003), an anomaly detection method which ignores noisy data is developed using the Robust SVM (RSVM) is presented.”
Outside the Closed World: On Using Machine Learning for Network Intrusion Detection (2010, background): “Often, they borrow schemes from the machine learning community, such as information theory [11], neural networks [12], support vector machines [13], genetic algorithms [14], artiﬁcial immune-systems [15], and many more.”
NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage (2019, unknown): “Researchers have also proposed to automatically detect attacks using machine learning [38], [64].”