The growth of the mutual fund industry and the accompanying competition among intermediaries should lead to progressively lower costs to shareholders, based on economic theory. This dissertation is comprised of three studies which examine shareholder costs among mutual funds to test this theory. In each study the expense ratios of mutual funds are examined, while one study also includes an examination of commission structures. In Essay 1, the effect of participation in a supermarket No Transaction Fee program on a fund’s expense ratio is examined. In addition, the change in characteristics of these participants during a difficult market period is studied. Essay 1 finds that NTF participation leads to higher initial expense ratios but that continued participation depends on the program’s ability to pay for itself. In Essay 2, market segmentation within the fund industry is examined for this same time period. Essay 2 finds increased market segmentation over a five year period and finds evidence of competitive pricing only among certain segments. Retail investors who invest in no-load funds appear to benefit from competitive pricing more than those who pay commissions. There is evidence of cost shifting during this time period, as funds lower expense ratios but increase commissions. In Essay 3, expense ratios of common funds within state-sponsored defined contribution plans are examined. Essay 3 finds evidence of market segmentation among the various states. Plan size may have some effect on the setting of expense ratios, but the effect does not appear to be economically significant. Number of participants has no significant effect on the expense ratio. State population displays some significance, such that funds actually charge more for larger states. Wealth of the state, on the other hand, may result in lower expense ratios. Overall, competitive pricing within the mutual fund industry is limited to certain market segments and may be dependent on the channel of distribution.

Radio-frequency (RF) fingerprinting is a process that uses the minute inconsistencies among manufactured radio transmitters to identify wireless devices. Coupled with location fingerprinting, which is a machine learning technique to locate devices based on their radio signals, it can uniquely identify and locate both trusted and rogue wireless devices transmitting over the air. This can have wide-ranging applications for the Internet of Things, security, and networking fields. To contribute to this effort, this research first builds a software-defined radio (SDR) testbed to collect an RF dataset over LTE and WiFi channels. The developed testbed consists of both hardware which are receivers with multiple antennas and software which performs signal preprocessing. Several features that can be used for RF device fingerprinting and location fingerprinting, including received signal strength indicator and channel state information, are also extracted from the signals. With the developed dataset, several data-driven machine learning algorithms have been implemented and tested for fingerprinting performance evaluation. Overall, experimental results show promising performance with a radio fingerprinting accuracy above 90\% and device localization within 1.10 meters.

Software Defined Networks (SDN) has created great potential and hope to overcome the need for secure, reliable and well managed next generation networks to drive effective service delivery on the go and meet the demand for high data rate and seamless connectivity expected by users. Thus, it is a network technology that is set to enhance our day-to-day activities. As network usage and reliance on computer technology are increasing and popular, users with bad intentions exploit the inherent weakness of this technology to render targeted services unavailable to legitimate users. Among the security weaknesses of SDN is Distributed Denial of Service (DDoS) attacks. Even though DDoS attack strategy is known, the number of successful DDoS attacks launched has seen an increment at an alarming rate over the last decade. Existing detection mechanisms depend on signatures of known attacks which has not been successful in detecting unknown or different shades of DDoS attacks. Therefore, a novel detection mechanism that relies on deviation from confidence interval obtained from the normal distribution of throughput polled without attack from the server. Furthermore, sensitivity analysis to determine which of the network metrics (jitter, throughput and response time) is more sensitive to attack by introducing white Gaussian noise and evaluating the local sensitivity using feed-forward artificial neural network is evaluated. All metrics are sensitive in detecting DDoS attacks. However, jitter appears to be the most sensitive to attack. As a result, the developed framework provides an avenue to make the SDN technology more robust and secure to DDoS attacks.

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