Fraud detection for online banking for scalable and distributed data

Online fraud causes billions of dollars in losses for banks. Therefore, online banking fraud detection is an important field of study. However, there are many challenges in conducting research in fraud detection. One of the constraints is due to unavailability of bank datasets for research or the required characteristics of the attributes of the data are not available. Numeric data usually provides better performance for machine learning algorithms. Most transaction data however have categorical, or nominal features as well. Moreover, some platforms such as Apache Spark only recognizes numeric data. So, there is a need to use techniques e.g. One-hot encoding (OHE) to transform categorical features to numerical features, however OHE has challenges including the sparseness of transformed data and that the distinct values of an attribute are not always known in advance. Efficient feature engineering can improve the algorithm’s performance but usually requires detailed domain knowledge to identify correct features. Techniques like Ripple Down Rules (RDR) are suitable for fraud detection because of their low maintenance and incremental learning features. However, high classification accuracy on mixed datasets, especially for scalable data is challenging. Evaluation of RDR on distributed platforms is also challenging as it is not available on these platforms. The thesis proposes the following solutions to these challenges: • We developed a technique Highly Correlated Rule Based Uniformly Distribution (HCRUD) to generate highly correlated rule-based uniformly-distributed synthetic data. • We developed a technique One-hot Encoded Extended Compact (OHE-EC) to transform categorical features to numeric features by compacting sparse-data even if all distinct values are unknown. • We developed a technique Feature Engineering and Compact Unified Expressions (FECUE) to improve model efficiency through feature engineering where the domain of the data is not known in advance. • A Unified Expression RDR fraud deduction technique (UE-RDR) for Big data has been proposed and evaluated on the Spark platform. Empirical tests were executed on multi-node Hadoop cluster using well-known classifiers on bank data, synthetic bank datasets and publicly available datasets from UCI repository. These evaluations demonstrated substantial improvements in terms of classification accuracy, ruleset compactness and execution speed.Doctor of Philosoph

Video game censorship is authoritarians’ latest tool to muzzle speech

The &ldquo;Blitzchung affair,&rdquo; as it came to be known, highlights how video games pose unique challenges to free speech. Western companies complying with Chinese censorship demands&mdash;in this case, attempting to suppress advocacy for a free Hong Kong&mdash;as a cost of doing business isn&rsquo;t new, but the role of video games as an important venue for speech and a central battleground for free speech remains underappreciated. Conflicts over free speech in video games go far beyond Hearthstone. Whether in the chat features of video games or in the narrative decisions made by video game designers, the censorship demands of countries around the world are increasingly shaping the digital entertainment consumed by the world&rsquo;s more than three billion gamers. These demands create a difficult challenge for video game companies: balancing the need for business growth with a commitment to free speech. &nbsp; &nbsp;</p

A Wolf in Sheep\u27s Clothing: Wolf versus Ashcroft and the Constitutionality of using the MPAA Ratings to Censor Films in Prison

Part I of this article looks at the history of the federal courts\u27 jurisprudence in deciding prisoner\u27s rights cases, culminating in the current test adopted in on Turner v. Safley. Part II considers the purposes behind the Zimmer Amendment and looks at the district and appellate court rulings in the Pennsylvania prisoners\u27 case, Wolf v. Ashcroft. Part III looks at the history of the MPAA ratings and cases dealing with their legal enforceability. Finally, Part IV applies Turner\u27s test to the Zimmer Amendment and the Pennsylvania policy prohibiting R, X, and NC-17 movies from being shown in prison, ultimately concluding that the Zimmer Amendment is unconstitutional because it impermissibly relies on the MPAA ratings

Aberrant Consumers: Selfies and Fat Admiration Websites

In contemporary consumer culture, the healthy body acts as a sign-value for success, a strong work ethic and self-control; it is viewed as a productive resource and medium for creating “bodily capital.” But there is a conflict at the heart of consumer culture, between the imperative to work hard and delay gratification, and the consumer dictum of instant pleasure. Health demonstrates the individuals’ ability to balance the opposing forces of production and consumption. Overtly fat and thin bodies signify an inability to balance the conflict. In this article, I compare different forms of self-presentation on social networking sites and online platforms to explore sign-values of the body in contemporary consumer culture. Websites such as Fantasy Feeder offer advice on how to gain social security benefits, and use fast food industry “bundling techniques” to maximize calorie intake with minimal cost suggesting that fat admiration participants are disruptive to social and economic ideals. I use Marxist and psychoanalytical theories to interpret photographs of “unhealthy” bodies to build a theoretical model for potentially disruptive figures in capitalist society

Large-Scale Emulation of Anonymous Communication Networks

Tor is the most popular low-latency anonymous communication system for the Internet, helping people to protect their privacy online and circumvent Internet censorship. Its low- latency anonymity and distributed design present a variety of open research questions related to — but not limited to — anonymity, performance, and scalability, that have generated considerable interest in the research community. Testing changes to the design of the protocol or studying attacks against it in the live network is undesirable as doing so can invade the privacy of users and even put them in harm’s way. Traditional Tor research has been limited to emulating a few hundred nodes with the ModelNet network emulator, or, simulating thousands of nodes with the Shadow discrete-event simulator, both of which may not accurately represent the real-world Tor network. We present SNEAC (Scalable Network Emulator for Anonymous Communication; pronounced "sneak"), a large-scale network emulator that allows us to emulate a network with thousands of nodes. Our hope is that with such large-scale experimentation, we can more closely emulate the live Tor network with half a million users

Spartan Daily, November 13, 1959

Volume 47, Issue 37https://scholarworks.sjsu.edu/spartandaily/3953/thumbnail.jp