by Argiriou, Nikos; Georgiadis, Leonidas

The problem of channel sharing by rate adaptive streams belonging to various classes is considered. Rate adaptation provides the opportunity for accepting more connections by adapting the bandwidth of connections that are already in the system. However, bandwidth adaptation must be employed in a careful manner in order to ensure that (a) bandwidth is allocated to various classes in a fair manner (system perspective) and (b) bandwidth adaptation does not affect adversely the perceived user quality of the connection (user quality). The system perspective aspect has been studied earlier. This paper focuses on the equally important user perspective. It is proposed to quantify user Quality of Service (QoS) through measures capturing short and long-term bandwidth fluctuations that can be implemented with the mechanisms of traffic regulators, widely used in networking for the purpose of controlling the traffic entering or exiting a network node. Furthermore, it is indicated how to integrate the user perspective metrics with the optimal algorithms for system performance metrics developed earlier by the authors. Simulation results illustrate the effectiveness of the proposed framework.

DOI: 10.1007/s10922-008-9101-5
Online Date: 8/20/2008
Print publication date: 12/1/2008
View article on SpringerLink

by Ohta, Satoru; Kurebayashi, Ryosuke; Kobayashi, Kiyoshi

Machine learning or data mining technologies are often used in network intrusion detection systems. An intrusion detection system based on machine learning utilizes a classifier to infer the current state from the observed traffic attributes. The problem with learning-based intrusion detection is that it leads to false positives and so incurs unnecessary additional operation costs. This paper investigates a method to decrease the false positives generated by an intrusion detection system that employs a decision tree as its classifier. The paper first points out that the information-gain criterion used in previous studies to select the attributes in the tree-constructing algorithm is not effective in achieving low false positive rates. Instead of the information-gain criterion, this paper proposes a new function that evaluates the goodness of an attribute by considering the significance of error types. The proposed function can successfully choose an attribute that suppresses false positives from the given attribute set and the effectiveness of using it is confirmed experimentally. This paper also examines the more trivial leaf rewriting approach to benchmark the proposed method. The comparison shows that the proposed attribute evaluation function yields better solutions than the leaf rewriting approach.

DOI: 10.1007/s10922-008-9102-4
Online Date: 8/15/2008
Print publication date: 12/1/2008
View article on SpringerLink