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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

Using Hierarchical Temporal Memory for Detecting Anomalous Network Activity

Bonhoff, Gerod M.

Hardcover

General ComputersComputer Security

ISBN10: 1025124235
ISBN13: 9781025124230
Publisher: Hutson Street Press
Published: May 22 2025
Pages: 158
Weight: 0.88
Height: 0.44 Width: 6.14 Depth: 9.21
Language: English

This research is motivated by the creation of intelligently autonomous cybercraft to reside in the intangible environment of cyberspace and maintain domain superiority. Specifically, this paper offers 7 challenges to the development of such a cybercraft. The focus is analysis of the claims 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 primary objective is to provide evidence that HTM facilitates accurate predictions of unusual worlds. The second objective is to 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 define network traffic (a major aspect of cyberspace) as benign or malicious is evaluated. Through the course of testing the performance of this implementation is poor. An independent algorithm is developed from a variant understanding of HTM theory. This alternate algorithm is independent of cyberspace and developed solely (but also in a contrived abstract world) to lend credibility to the use of prediction as a method of testing intelligence.

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