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Filtering by: Committee Member Eveleigh, Timothy Degree Ph.D. Keyword Adaptive Boosting

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Application of a Novel Multiple Kernel Learning Framework for Improving the Robustness of Network Intrusion Detection

Description:

Current day Network Intrusion Detection Systems have several shortcomings such as high rates of false positive alerts, low detection rates of rare but dangerous attacks and the need for constant human intervention and tuning. Daily reports of incidents appear in public media including major...

Author:

Fossaceca, John M.

Keyword:

Systems Engineering, Machine Learning, Pattern Recognition, Adaptive Boosting, Multiple Kernel Learning, and Cybersecurity

GW Unit:

School of Engineering and Applied Science

Degree:

Ph.D.

Advisor:

Sarkani, Shahram and Mazzuchi, Thomas A

Committee Member(s):

Eveleigh, Timothy, Holzer, Thomas, and Murphree, E. Lile

Resource Type:

Thesis or Dissertation