New methods of attacking networks are being invented at an alarming rate, and
pure signature detection cannot keep up. The ability of intrusion detection systems to
generalize to new attacks based on behavior is of increasing value. Machine Learning
algorithms have been successfully applied to intrusion and fraud detection; however
the time and accuracy tradeoffs between algorithms are not always considered when
faced with such a broad range of choices. This thesis explores the time and accuracy metrics of a wide variety of machine learning algorithms, using a purpose-built
supervised learning dataset. Topics covered include dataset dimensionality reduction
through pre-processing techniques, training and testing times, classification accuracy,
and performance tradeoffs. Further, ensemble learning and meta-classification are
used to explore combinations of the algorithms and derived data sets, to examine the
effects of homogeneous and heterogeneous aggregations. The results of this research
are presented with observations and guidelines for choosing learning schemes in this
domain.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:MWU.1993/22077 |
| Date | 22 August 2013 |
| Creators | Peters, Chad |
| Contributors | Anderson, John (Computer Science) Baltes, Jacky (Computer Science), Scuse, David (Computer Science) McNeill, Dean (Electrical & Computer Engineering) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Detected Language | English |
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