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Title page for ETD etd-12062007-095827


Type of Document Master's Thesis
Author Lauf, Adrian Peter
Author's Email Address adrian.p.lauf@vanderbilt.edu
URN etd-12062007-095827
Title HybrIDS: Embeddable Hybrid Intrusion Detection System
Degree Master of Science
Department Electrical Engineering
Advisory Committee
Advisor Name Title
Richard A. Peters Committee Member
William H. Robinson Committee Member
Keywords
  • machine learning
  • embeddable
  • intrusion
  • detection
  • hybrid
  • Computer security -- Computer programs
  • Computer networks -- Security measures -- Computer programs
Date of Defense 2007-09-27
Availability unrestricted
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.

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