The metric of entropy provides a measure about the randomness of data and a measure of information gained by comparing different attributes. Intrusion detection systems can collect very large amounts of data, which are not necessarily manageable by manual means. Collected intrusion detection data often contains redundant, duplicate, and irrelevant entries, which makes analysis computationally intensive likely leading to unreliable results. Reducing the data to what is relevant and pertinent to the analysis requires the use of data mining techniques and statistics. Identifying patterns in the data is part of analysis for intrusion detections in which the patterns are categorized as normal or anomalous. Anomalous data needs to be further characterized to determine if representative attacks to the network are in progress. Often time subtleties in the data may be too muted to identify certain types of attacks. Many statistics including entropy are used in a number of analysis techniques for identifying attacks, but these analyzes can be improved upon. This research expands the use of Approximate entropy and Sample entropy for feature selection and attack analysis to identify specific types of subtle attacks to network systems. Through enhanced analysis techniques using entropy, the granularity of feature selection and attack identification is improved.
| Identifer | oai:union.ndltd.org:nova.edu/oai:nsuworks.nova.edu:gscis_etd-1368 |
| Date | 01 January 2015 |
| Creators | Acker, Frank |
| Publisher | NSUWorks |
| Source Sets | Nova Southeastern University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | CEC Theses and Dissertations |
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