Probabilistic classification of quality of service in wireless computer networks

There is an increasing reliance on wireless computer networks for communicating various types of time sensitive applications such as voice over internet protocol (VoIP). Quality of service (QoS) can play an important role in wireless computer networks as it can facilitate evaluation of their performance and can provide mechanisms to improve their operation. In this study probabilistic neural network (PNN) and Bayesian classification were developed to process delay, jitter and percentage packet loss ratio for VoIP traffic. Both methods successfully categorized the transmission of VoIP packets into low, medium and high QoS categories but overall the Bayesian approach performed more accurately than PNN. By accurately determining the network's QoS, an improved understanding of its performance is obtained

Network anomaly detection research: a survey

Data analysis to identifying attacks/anomalies is a crucial task in anomaly detection and network anomaly detection itself is an important issue in network security. Researchers have developed methods and algorithms for the improvement of the anomaly detection system. At the same time, survey papers on anomaly detection researches are available. Nevertheless, this paper attempts to analyze futher and to provide alternative taxonomy on anomaly detection researches focusing on methods, types of anomalies, data repositories, outlier identity and the most used data type. In addition, this paper summarizes information on application network categories of the existing studies