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Computationally efficient Neural Network Intrusion Security Awareness

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2 Author(s)
Vollmer, T. ; Idaho Nat. Lab., Idaho Falls, ID, USA ; Manic, M.

An enhanced version of an algorithm to provide anomaly based intrusion detection alerts for cyber security state awareness is detailed. A unique aspect is the training of an error back-propagation neural network with intrusion detection rule features to provide a recognition basis. Ethernet network packet details are subsequently provided to the trained network to produce a classification. This leverages rule knowledge sets to produce classifications for anomaly based systems. Several test cases executed on ICMP protocol revealed a 60% identification rate of true positives. This rate matched the previous work, but 70% less memory was used and the run time was reduced to less than 1 second from 37 seconds.

Published in:

Resilient Control Systems, 2009. ISRCS '09. 2nd International Symposium on

Date of Conference:

11-13 Aug. 2009