Modeling network traffic on a global network-centric system with artificial neural networks

This dissertation proposes a new methodology for modeling and predicting network traffic. It features an adaptive architecture based on artificial neural networks and is especially suited for large-scale, global, network-centric systems. Accurate characterization and prediction of network traffic is essential for network resource sizing and real-time network traffic management. As networks continue to increase in size and complexity, the task has become increasingly difficult and current methodology is not sufficiently adaptable or scaleable. Current methods model network traffic with express mathematical equations which are not easily maintained or adjusted. The accuracy of these models is based on detailed characterization of the traffic stream which is measured at points along the network where the data is often subject to constant variation and rapid evolution. The main contribution of this dissertation is development of a methodology that allows utilization of artificial neural networks with increased capability for adaptation and scalability. Application on an operating global, broadband network, the Connexion by Boeingʼ network, was evaluated to establish feasibility. A simulation model was constructed and testing was conducted with operational scenarios to demonstrate applicability on the case study network and to evaluate improvements in accuracy over existing methods --Abstract, page iii

Integrating data warehouses with web data : a survey

This paper surveys the most relevant research on combining Data Warehouse (DW) and Web data. It studies the XML technologies that are currently being used to integrate, store, query, and retrieve Web data and their application to DWs. The paper reviews different DW distributed architectures and the use of XML languages as an integration tool in these systems. It also introduces the problem of dealing with semistructured data in a DW. It studies Web data repositories, the design of multidimensional databases for XML data sources, and the XML extensions of OnLine Analytical Processing techniques. The paper addresses the application of information retrieval technology in a DW to exploit text-rich document collections. The authors hope that the paper will help to discover the main limitations and opportunities that offer the combination of the DW and the Web fields, as well as to identify open research line

Fraudsters, Churches, Economy, and the Expectations Gap: Applying Trends of Occupational Fraud to an Assurance Engagement Team Plan and Fraud-Prevention Client Proposal

The purpose of this thesis is to present an overview of fraud, including concepts, trends, and controls to in turn, develop an effective assurance work plan as well as a fraud-prevention proposal to a potential client. When KPMG collected data from 348 of their company fraud investigations in 2011, an average of 87 percent were male (3). Around thirty-two percent of fraudsters usually worked in a finance role which gave them access to assets and financial statements. According to Donald Cressy’s research, it takes all three elements to be considered fraud: a triangle of motivation, opportunity, and rationalization. However, in the 2004 CPA Journal, David T. Wolfe and Dana R. Hermanson discussed the addition of another element from their research to create a fraud diamond, which also includes the individual’s capacity. Compared to public companies, fraud occurs more frequently in privately owned companies. Nearly 40 percent of victim organizations in the Association of Certified Fraud Examiners’ 2012 study were privately owned while 28 percent were publicly traded. Furthermore, fraud is more likely to be detected by individuals in the internal or external audit setting or an anonymous tipline. These concepts are explained further in sections of the assurance engagement team plan and fraud-prevention proposal to a small business owner