Goss, Ryan and Nitschke, Geoff (2017) Automated Pattern Identification and Classification: Anomaly Detection Case Study, Proceedings of Genetic and Evolutionary Computation Conference (GECCO 2017), Berlin, Germany., 59-60, ACM Press.
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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.
| Item Type: | Conference poster |
|---|---|
| Subjects: | Computing methodologies > Artificial intelligence |
| Date Deposited: | 23 Nov 2017 |
| Last Modified: | 10 Oct 2019 15:31 |
| URI: | http://pubs.cs.uct.ac.za/id/eprint/1184 |
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