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Characterizing and Detecting Unrevealed Elements of Network Systems
Characterizing and Detecting Unrevealed Elements of Network Systems
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This dissertation addresses the problem of discovering and characterizing unknown elements in network systems. It is not uncommon to have incomplete knowledge of network systems due to either passive circumstances, e.g. limited resources to observe a network, or active circumstances, e.g. intentional acts of concealment, or some combination of active and passive influences. This research suggests statistical and graph theoretic approaches for such situations, including those in which nodes are…
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  • ISBN-10: 1249594790
  • ISBN-13: 9781249594796
  • Format: 18.9 x 24.6 x 0.9 cm, minkšti viršeliai
  • Language: English
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This dissertation addresses the problem of discovering and characterizing unknown elements in network systems. It is not uncommon to have incomplete knowledge of network systems due to either passive circumstances, e.g. limited resources to observe a network, or active circumstances, e.g. intentional acts of concealment, or some combination of active and passive influences. This research suggests statistical and graph theoretic approaches for such situations, including those in which nodes are causally related. A related aspect of this research is accuracy assessment. It is possible an analyst could fail to detect a network element, or be aware of network elements, but incorrectly conclude the associated network system structure. Consequently, this dissertation provides a framework to evaluate accuracy.

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  • Author: James A Leinart
  • Publisher:
  • ISBN-10: 1249594790
  • ISBN-13: 9781249594796
  • Format: 18.9 x 24.6 x 0.9 cm, minkšti viršeliai
  • Language: English English

This dissertation addresses the problem of discovering and characterizing unknown elements in network systems. It is not uncommon to have incomplete knowledge of network systems due to either passive circumstances, e.g. limited resources to observe a network, or active circumstances, e.g. intentional acts of concealment, or some combination of active and passive influences. This research suggests statistical and graph theoretic approaches for such situations, including those in which nodes are causally related. A related aspect of this research is accuracy assessment. It is possible an analyst could fail to detect a network element, or be aware of network elements, but incorrectly conclude the associated network system structure. Consequently, this dissertation provides a framework to evaluate accuracy.

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