Machine learning techniques for the computer security domain of anomaly detection

134 citations recorded by the source corpus.

How researchers see this paper

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

Research Priorities for Robust and Beneficial Artificial Intelligence (2015, methodology): “These techniques could be applied to the detection of intrusions [43], analyzing malware [44], or detecting potential exploits in other programs through code analysis [45].”
Learning Universal Adversarial Perturbations with Generative Models (2017, background): “Machine Learning models are increasingly relied upon for safety and business critical tasks such as in medicine [23], [29], [39], robotics and automotive [27], [31], [38], security [2], [17], [36] and ﬁnancial [13], [18], [34] applications.”
Anomaly Detection for Discrete Sequences: A Survey (2012, methodology): “Lane’s PhD thesis [44] discusses other variants of the above described similarity measure in the context of anomaly detection.”