UCT CS Research Document Archive

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. .

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Abstract

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
ID Code:1184
Deposited By:Nitschke, Geoff
Deposited On:23 November 2017