dc.creator | Lauf, Adrian Peter | |
dc.date.accessioned | 2020-08-23T16:14:29Z | |
dc.date.available | 2009-12-18 | |
dc.date.issued | 2007-12-18 | |
dc.identifier.uri | https://etd.library.vanderbilt.edu/etd-12062007-095827 | |
dc.identifier.uri | http://hdl.handle.net/1803/15166 | |
dc.description.abstract | In order to provide preventative security to a homogeneous device network,
techniques in addition to static encryption must be implemented to assure network
integrity by identifying possible deviant nodes within the collective. This thesis proposes
a set of algorithms and techniques for an intrusion detection system, which when
combined, provide a two-stage approach that seeks to reduce or eliminate training period
requirements, while providing multiple anomaly detection and a degree of self tuning. By
utilizing a high level of behavioral abstraction, these intrusion detection techniques can
be applied to a broad range of devices, network implementations, and scenarios. Each
device node is supplied with an embedded intrusion detection system which allows it to
monitor inter-device requests, enabling machine learning techniques for purposes of
deviant node analysis. The two principal methods, a maxima detection scheme, and a
cross-correlative detection scheme, are combined to create a two-phase detection scheme
that can successfully determine deviant node pervasion percentages of up to 22% within
the homogeneous device network. | |
dc.format.mimetype | application/pdf | |
dc.subject | machine learning | |
dc.subject | embeddable | |
dc.subject | intrusion | |
dc.subject | detection | |
dc.subject | hybrid | |
dc.subject | Computer security -- Computer programs | |
dc.subject | Computer networks -- Security measures -- Computer programs | |
dc.title | HybrIDS: Embeddable Hybrid Intrusion Detection System | |
dc.type | thesis | |
dc.contributor.committeeMember | Richard A. Peters | |
dc.contributor.committeeMember | William H. Robinson | |
dc.type.material | text | |
thesis.degree.name | MS | |
thesis.degree.level | thesis | |
thesis.degree.discipline | Electrical Engineering | |
thesis.degree.grantor | Vanderbilt University | |
local.embargo.terms | 2009-12-18 | |
local.embargo.lift | 2009-12-18 | |