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Rayman D. Meservy
Brigham Young University
12Publications
5H-index
131Citations
Publications 12
Newest
#1Thomas O. Meservy (BYU: Brigham Young University)H-Index: 7
#2Kelly J. Fadel (USU: Utah State University)H-Index: 9
Last.Rayman D. Meservy (BYU: Brigham Young University)H-Index: 5
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Jan 1, 2010 in AMCIS (Americas Conference on Information Systems)
#1James V. Hansen (BYU: Brigham Young University)H-Index: 21
#2Paul Benjamin Lowry (HKU: University of Hong Kong)H-Index: 35
Last.Rayman D. Meservy (BYU: Brigham Young University)H-Index: 5
view all 3 authors...
In The extensive growth of computing networks and tools and tricks for intruding into and attacking networks has underscored the importance of intrusion detection in network security. Yet, contemporary intrusion detection systems (IDS) are limiting in that they typically employ a misuse detection strategy, with searches for patterns of program or user behavior that match known intrusion scenarios, or signatures. Accordingly, there is a need for more robust and adaptive methods for designing and ...
#1James V. Hansen (BYU: Brigham Young University)H-Index: 21
#2Paul Benjamin Lowry (BYU: Brigham Young University)H-Index: 35
Last.Daniel McDonald (UA: University of Arizona)H-Index: 9
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Because malicious intrusions into critical information infrastructures are essential to the success of cyberterrorists, effective intrusion detection is also essential for defending such infrastructures. Cyberterrorism thrives on the development of new technologies; and, in response, intrusion detection methods must be robust and adaptive, as well as efficient. We hypothesize that genetic programming algorithms can aid in this endeavor. To investigate this proposition, we conducted an experiment...
#1James V. HansenH-Index: 21
#2Paul Benjamin LowryH-Index: 35
Last.Rayman D. MeservyH-Index: 5
view all 0 authors...
#1James V. Hansen (BYU: Brigham Young University)H-Index: 21
#2Rayman D. Meservy (BYU: Brigham Young University)H-Index: 5
This paper reports a study unifying optimization by genetic algorithm with a generalized regression neural network. Experiments compare hill-climbing optimization with that of a genetic algorithm, both in conjunction with a generalized regression neural network. Controlled data with nine independent variables are used in combination with conjunctive and compensatory decision forms, having zero percent and 10 percent noise levels. Results consistently favor the GRNN unified with the genetic algor...
#1James V. Hansen (BYU: Brigham Young University)H-Index: 21
#2Rayman D. Meservy (BYU: Brigham Young University)H-Index: 5
Last.Larry E. Wood (BYU: Brigham Young University)H-Index: 10
view all 3 authors...
Decision-support systems can be improved by enabling them to use past decisions to assist in making present ones. Reasoning from relevant past cases is appealing because it corresponds to some of the processes an expert uses to solve problems quickly and accurately. All this depends on an effective method of organizing cases for retrieval. This paper investigates the use of inductive networks as a means for case organization and outlines an approach to determining the desired number of cases-or ...
#1James V. Hansen (BYU: Brigham Young University)H-Index: 21
#2Rayman D. Meservy (BYU: Brigham Young University)H-Index: 5
Last.Larry E. Wood (BYU: Brigham Young University)H-Index: 10
view all 3 authors...
Case-based reasoning is thought to aid decision making because it mimics the way in which humans analyze problems. Effective recall of case situations that are similar to a current situation is a key component of this type of decision support. This paper shows how the concept of rank can be used to create compact indexing trees for recall of similar cases. We further consider how pruning can be applied to improving compactness and methods for measuring the reliability of the result.
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