Assessing Wireless Network Dependability through Knowledge Extraction via Decision Trees

Critical infrastructures such as wireless network systems demand dependability. Dependability attributes reported here include availability, reliability, maintainability and survivability (ARMS). This research uses computer simulation and knowledge extraction to introduce a new approach to measure dependability of wireless networks. Earlier research has used computer simulation for estimating wireless network dependability. This work introduces a new methodology which uses discrete time event simulation in-put/output to train an artificial neural network and then extract knowledge via decision trees. A comparison of decision tree extraction technique results are discussed, including those from neural (TREPAN) and non neural networks (C4.5). Significant insights are gained into increasing wireless infrastructure dependability through such knowledge extraction techniques; however the neural approach is superior from a parsimonious and comprehensibility perspective.