Multifractality in TCP/IP traffic: the case against
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Non-Gaussian and Long Memory Statistical Characterizations for Internet Traffic with Anomalies
IEEE Transactions on Dependable and Secure Computing
Visualization and inference based on wavelet coefficients, SiZer and SiNos
Computational Statistics & Data Analysis
Passive analysis of TCP anomalies
Computer Networks: The International Journal of Computer and Telecommunications Networking
Multifractality in TCP/IP traffic: the case against
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Long range dependent trafic
Detection of multiple changes in fractional integrated ARMA processes
IEEE Transactions on Signal Processing
QoS-IP'05 Proceedings of the Third international conference on Quality of Service in Multiservice IP Networks
A longitudinal study of small-time scaling behavior of internet traffic
NETWORKING'10 Proceedings of the 9th IFIP TC 6 international conference on Networking
A scaling analysis of UMTS traffic
NEW2AN'06 Proceedings of the 6th international conference on Next Generation Teletraffic and Wired/Wireless Advanced Networking
TCP's role in the propagation of self-similarity in the Internet
Computer Communications
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A statistical test is described for determining if scaling exponents vary over time. It is applicable to diverse scaling phenomena including long-range dependence and exactly self-similar processes in a uniform framework without the need for prior knowledge of the type in question. It is based on the special properties of wavelet-based estimates of the scaling exponent, strongly motivating an idealized inference problem: the equality or otherwise of means of independent Gaussian variables with known variances. A uniformly most powerful invariant test exists for this problem and is described. A separate uniformly most powerful invariant test is also given for when the scaling exponent undergoes a level change. The power functions of both tests are given explicitly and compared. Using simulation, the effect, in practice, of deviations from the idealizations made of the statistical properties of the wavelet detail coefficients are analyzed and found to be small. The tests inherit the significant robustness and computational advantages of the underlying wavelet-based estimator. A detailed methodology is given, describing its use in practical situations. The use and benefits of the test are illustrated on the Bellcore Ethernet data sets