Chen, Liang. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2013. / Includes bibliographical references (leaves 164-175). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts also in Chinese.

Digitized by Kansas Correctional Industries

As more data are observed over time, investigating the variation across time has become a vital part of analyzing such data. In this dissertation, we discuss varying-coefficient models and functional data analysis methods for temporally heterogenous data. More specifically, we examine two different types of temporal heterogeneity. The first type of temporal heterogeneity stems from temporal evolution of relational pattern over time. Dynamic networks are commonly used when relational data are observed over time. Unlike static network analysis, dynamic network analysis emphasizes the importance of recognizing temporal evolution of relationship among observations. We propose and investigate a family of dynamic network models, known as varying-coefficient exponential random graph model (VCERGM), that characterize the evolution of network topology through smoothly varying parameters. The VCERGM directly provides an interpretable dynamic network model that enables the inference of temporal heterogeneity in dynamic networks. Furthermore, we introduce a method that analyzes multilevel dynamic networks. If there exist multiple relational data observed at one time point, it is reasonable to additionally consider the variability among the repeated observations at each time point. The proposed method is an extension of stochastic blockmodels with a priori block membership and temporal random effects. It incorporates a variability among multiple relational structures at one time point and provides a richer representation of dependent engagement patterns at each time point. The method is also flexible in analyzing networks with time-varying networks. Its smooth parameters can be interpreted as evolving strength of engagement within and across blocks. The second type of temporal heterogeneity is motivated by temporal shifts in continuously observed data. When multiple curves are obtained and there exists a common curvature shared by all the observed curves, understanding the common curvature may involve a preprocessing step of managing temporal shifts among curves. We explore the properties of continuous in-shoe sensor recordings to understand the source of variability in gait data. Our case study is based on measurements of three healthy subjects. The in-shoe sensor data we explore show both phase and amplitude variabilities; we separate these sources via curve registration. We examine the correlation of temporal shifts across sensors to evaluate the pattern of phase variability shared across sensors. We apply a series of functional data analysis approaches to the registered in-shoe sensor curves to examine their association with current gold-standard gait measurement, so called ground reaction force.

The second problem that is addressed in the thesis is about traffic analysis and classification. Accurate identification of network applications is important to many network activities. Traditional port-based technique has become much less effective since many new applications no longer use well-known fixed port numbers. In this thesis, we propose a novel profile-based approach to identify traffic flows belonging to the target application. In contrast to classifying traffic based on statistics of individual flows in previous studies, we build behavioral profiles of the target application, which describe dominant communication patterns of the application. Based on the behavior profiles, a two-level matching is used in identifying new traffic. We demonstrate the effectiveness of our method on campus traffic traces. Our results show that one can identify the popular P2P applications with very high accuracy. / This thesis represents new intelligent methods for monitoring and classifying network traffic. Internet traffic flow measurement is vitally important for network management, accounting and performance studies. Cisco's NetFlow is a widely deployed flow measurement solution that uses a configurable static sampling rate to control processor and memory usage on the router and the amount of reporting flow records generated. But during flooding attacks the memory and network bandwidth consumed by flow records can increase beyond what is available. Currently available countermeasures have their own problems In this thesis, we propose an entropy based adaptive flow aggregation algorithm. Relying on information-theoretic techniques, the algorithm efficiently identifies the clusters of attack flows in real time and aggregates those large number of short attack flows into a few metaflows. Compared to currently available solutions, our solution not only alleviates the problem in memory and export bandwidth, but also significantly improves the accuracy of legitimate flows. We evaluate our system using both synthetic trace file and real trace files from the Internet. / Hu, Yan. / Adviser: Dah-Mino Chen. / Source: Dissertation Abstracts International, Volume: 70-06, Section: B, page: 3600. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 128-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

Lam, Chi Ho. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 113-117). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.vi / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Organization --- p.4 / Chapter 2 --- Related Works and Background --- p.7 / Chapter 2.1 --- Related Works --- p.7 / Chapter 2.2 --- Background --- p.9 / Chapter 2.2.1 --- Datalite --- p.9 / Chapter 2.2.2 --- Proportional Union Method --- p.14 / Chapter 2.2.3 --- Quasi-Likelihood Approach --- p.18 / Chapter 3 --- Estimation Error of Existing TD-based TMA schemes --- p.24 / Chapter 3.1 --- Error Accumulation and Amplification of Existing Schemes --- p.25 / Chapter 3.1.1 --- Pu --- p.25 / Chapter 3.1.2 --- Qmle --- p.26 / Chapter 3.1.3 --- Datalite --- p.26 / Chapter 3.2 --- Estimation Error of 3-sets intersection cases --- p.28 / Chapter 3.2.1 --- Pu --- p.28 / Chapter 3.2.2 --- Datalite --- p.30 / Chapter 4 --- Error Reduction Via Traffic Digest Splitting --- p.36 / Chapter 4.1 --- Motivation --- p.36 / Chapter 4.2 --- Objective Functions for Optimal TD-splitting --- p.39 / Chapter 4.3 --- Problem Formulation of Threshold-based Splitting --- p.41 / Chapter 4.3.1 --- Minimizing Maximum Estimation Error --- p.42 / Chapter 4.3.2 --- Minimizing R.M.S. Estimation Error --- p.46 / Chapter 4.4 --- Analysis of Estimation Error Reduction Via Single-Level TD-splitting --- p.48 / Chapter 4.4.1 --- Noise-to-signal Ratio Reduction --- p.49 / Chapter 4.4.2 --- Estimation Error Reduction --- p.52 / Chapter 4.5 --- Recursive Splitting --- p.56 / Chapter 4.5.1 --- Minimizing Maximum Estimation Error --- p.57 / Chapter 4.5.2 --- Minimizing R.M.S. Estimation Error --- p.59 / Chapter 5 --- Realization of TD-splitting for Network Traffic Measurement --- p.61 / Chapter 5.1 --- Tracking Sub-TD Membership --- p.64 / Chapter 5.1.1 --- Controlling the Noise due to Non-Existent Flows on a Target Link --- p.64 / Chapter 5.1.2 --- Sub-TD Membership Tracking for Single-level TD-splitting --- p.65 / Chapter 5.1.3 --- Sub-TD Membership Tracking under Recursive Splitting --- p.66 / Chapter 5.2 --- Overall Operations to support TD-splitting for Network-wide Traffic Measurements --- p.67 / Chapter 5.2.1 --- Computation Time for TD-splitting --- p.69 / Chapter 6 --- Performance Evaluation --- p.72 / Chapter 6.1 --- Applying TD-splitting on Generic Network Topology --- p.72 / Chapter 6.1.1 --- Simulation Settings --- p.73 / Chapter 6.1.2 --- Validity of the Proposed Surrogate Objective Functions --- p.75 / Chapter 6.1.3 --- Performance of Single-level TD-splitting --- p.77 / Chapter 6.1.4 --- Performance of Recursive TD-splitting --- p.88 / Chapter 6.1.5 --- Heterogeneous NSR Loading --- p.95 / Chapter 6.2 --- Internet Trace Evaluation --- p.99 / Chapter 6.2.1 --- Simulation Results --- p.100 / Chapter 7 --- Conclusion --- p.105 / Chapter A --- Extension of QMLE for Cardinality Estimation of 3-sets Intersection --- p.107 / Bibliography --- p.113

by Adrian Segall. / Bibliography: leaf 29. / "May 1979." / Defense Advanced Projects Agency contract ONR/N00014-75-C-1183 Office of Naval Research contract ONR/N00014-77-C-0532

Thesis (Ph. D.)--University of Chicago Graduate School of Business, December 1997. / Includes bibliographical references.

Includes bibliographical references (p. 128-135).

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