Automated Pattern Identification and Classification: Anomaly Detection Case Study
Goss, Ryan and Geoff Nitschke (2017) Automated Pattern Identification and Classification: Anomaly Detection Case Study. In Proceedings Genetic and Evolutionary Computation Conference (GECCO 2017), pages 59-60, Berlin, Germany. .
In this study, the efficacy of the Automated Pattern Identification and Classification (APIC) Machine Learning (ML) pipeline method was evaluated as an Anomaly Intrusion Detection (AID) system to determine if using an ML-pipeline method could reduce false positive rates compared to similar methods using the same data set.
|EPrint Type:||Conference Poster|
|Subjects:||I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE|
|Deposited By:||Nitschke, Geoff|
|Deposited On:||23 November 2017|