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dc.contributor.authorRossell, Danielen_US
dc.date.accessioned2015-08-04T16:01:30Z
dc.date.available2015-08-04T16:01:30Z
dc.date.issued2013
dc.date.submitted2013
dc.identifier.other
dc.identifier.urihttps://hdl.handle.net/2144/12205
dc.descriptionThesis (M.S.)--Boston Universityen_US
dc.description.abstractThis thesis focuses on the problem of anomaly detection in computer networks. Anomalies are often malicious intrusion attempts that represent a serious threat to network security. Adaptive Resonance Theory (ART) is used as a classification scheme for identifying malicious network traffic. ART was originally developed as a theory to explain how the human eye categorizes visual patterns. For network intrusion detection, the core ART algorithm is implemented as a clustering algorithm that groups network traffic into clusters. A machine learning process allows the number of clusters to change over time to best conform to the data. Network traffic is characterized by network flows, which represent a packet, or series of packets, between two distinct nodes on a network. These flows can contain a number of attributes, including IP addresses, ports, size, and duration. These attributes form a multi-dimensional vector that is used in the clustering process. Once data is clustered along the defined dimensions, anomalies are identified as data points that do not match known good or nominal network traffic. The ART clustering algorithm is tested on a realistic network environment that was generated using the network flow simulation tool FS. The clustering results for this simulation show very promising detection rates for the ART clustering algorithm.en_US
dc.language.isoen_US
dc.publisherBoston Universityen_US
dc.titleAnomaly detection using adaptive resonance theoryen_US
dc.typeThesis/Dissertationen_US
etd.degree.nameMaster of Scienceen_US
etd.degree.levelmastersen_US
etd.degree.disciplineSystems Engineeringen_US
etd.degree.grantorBoston Universityen_US


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