Date of Award
Master of Science in Computer Science
Department of Electrical and Computer Engineering
Robert F. Mills, PhD
This thesis explores the nature of cyberspace and forms an argument for it as an intangible world. This research is motivated by the notion of creating intelligently autonomous cybercraft to reside in that environment and maintain domain superiority. Specifically, this paper offers 7 challenges associated with development of intelligent, autonomous cybercraft. The primary focus is an analysis of the claims of a machine learning language called Hierarchical Temporal Memory (HTM). In particular, HTM theory claims to facilitate intelligence in machines via accurate predictions. It further claims to be able to make accurate predictions of unusual worlds, like cyberspace. The research thrust of this thesis is then two fold. The primary objective is to provide supporting evidence for the conjecture that HTM implementations facilitate accurate predictions of unusual worlds. The second objective is to then lend evidence that prediction is a good indication of intelligence. A commercial implementation of HTM theory is tested as an anomaly detection system and its ability to characterize network traffic (a major component of cyberspace) as benign or malicious is evaluated. Through the course of testing the poor performance of this implementation is revealed and an independent algorithm is developed from a variant understanding of HTM theory. This alternate algorithm is independent of the realm of cyberspace and developed solely (but also in a contrived abstract world) to lend credibility to concept of using prediction as a method of testing intelligence.
DTIC Accession Number
Bonhoff, Gerod M., "Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity" (2008). Theses and Dissertations. 2746.