Computationally Efficient Neural Network Intrusion Security Awareness

Conference · Sat Aug 01 00:00:00 EDT 2009

DOI:https://doi.org/10.1109/ISRCS.2009.5251357· OSTI ID:968573

Vollmer, Todd; Manic, Milos

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. 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.

• Research Organization: Idaho National Lab. (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: DE-AC07-05ID14517
• OSTI ID: 968573
• Report Number(s): INL/CON-09-16248; TRN: US200924%%548
• Resource Relation: Conference: 2nd International Symposium on Resilient Control Systems 2009,Idaho Falls, ID,08/11/2009,08/13/2009
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING
ALGORITHMS
CLASSIFICATION
CONTROL SYSTEMS
DETECTION
NEURAL NETWORKS
SECURITY
TRAINING
backpropagation
neural networks
site security monitoring