Digital Commons Network^™

Novel Instance-Level Weighted Loss Function For Imbalanced Learning, Trent Geisler

Doctor of Data Science and Analytics Dissertations

Binary classification using imbalanced datasets remains a challenge. Typically, supervised learning algorithms minimize the binary cross-entropy objective function to determine the final parameter estimates. This objective function assumes an equal class distribution between the minority (i.e. events) and majority (i.e. non-events) classes, which almost never exists in real-world modeling. In the imbalanced data setting, the equal class distribution is grossly violated, and the resulting parameter estimates are biased toward the majority class. To overcome the bias and improve model generalization, we focus on modifying the original binary cross-entropy objective function by uniquely weighting each minority class observation. We base our …

Gr-175 - Credit Default Prediction Of Money Borrowing Companies Using Pyspark Framework, Jitendra Sai Kota, Mallika Boyapati

C-Day Computing Showcase

Credit-lending companies have resorted to the use of Machine Learning algorithms in the recent past to predict the probability of default of a customer for future credit lending purposes. Most credit companies view this as a binary classification problem of predicting whether an individual would default or not. Companies have been using models of Logistic Regression for a long time because of the explainability of the final feature set used in modeling. Explainability brings transparency to every stakeholder involved in the process. Other models like Neural Nets have achieved better accuracy scores, but the features generated by them are not …

Challenges Of Digital Privacy In Banking Organizations, Okechukwu Innocent Ogudebe

Walden Dissertations and Doctoral Studies

As the information and technology age becomes more advanced, digital privacy flaws have become more challenging. Information technology (IT) security managers, chief information security officers, and other stakeholders in banks are concerned with identity-based authentication attacks because identity-theft attacks cause data breaches. Grounded in the protection motivation theory, the purpose of this qualitative pragmatic study was to examine strategies IT security professionals working on internet banking platforms use to mitigate identity-based authentication attacks. The study participants comprised five IT security professionals currently working in the online banking industry from the northeastern United States with at least 5 years of experience …

Few-Shot Malware Detection Using A Novel Adversarial Reprogramming Model, Ekula Praveen Kumar

Browse all Theses and Dissertations

The increasing sophistication of malware has made detecting and defending against new strains a major challenge for cybersecurity. One promising approach to this problem is using machine learning techniques that extract representative features and train classification models to detect malware in an early stage. However, training such machine learning-based malware detection models represents a significant challenge that requires a large number of high-quality labeled data samples while it is very costly to obtain them in real-world scenarios. In other words, training machine learning models for malware detection requires the capability to learn from only a few labeled examples. To address …

Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu

Computer Science Faculty Publications

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …

Methods Of Anomaly Detection: An Applied Framework And Application To Health Economics, Nathaniel Eric Islip

EWU Masters Thesis Collection

No abstract provided.

The Application Of Deep Learning And Cloud Technologies To Data Science, Ian A. Trawinski

College of Graduate Studies: Theses & Dissertations

Machine Learning and Cloud Computing have become a staple to businesses and educational institutions over the recent years. The two forefronts of big data solutions have garnered technology giants to race for the superior implementation of both Machine Learning and Cloud Computing. The objective of this thesis is to test and utilize AWS SageMaker in three different applications: time-series forecasting with sentiment analysis, automated Machine Learning (AutoML), and finally anomaly detection. The first study covered is a sentiment-based LSTM for stock price prediction. The LSTM was created with two methods, the first being SQL Server Data Tools, and the second …

Exposing And Fixing Causes Of Inconsistency And Nondeterminism In Clustering Implementations, Xin Yin

Dissertations

Cluster analysis aka Clustering is used in myriad applications, including high-stakes domains, by millions of users. Clustering users should be able to assume that clustering implementations are correct, reliable, and for a given algorithm, interchangeable. Based on observations in a wide-range of real-world clustering implementations, this dissertation challenges the aforementioned assumptions.

This dissertation introduces an approach named SmokeOut that uses differential clustering to show that clustering implementations suffer from nondeterminism and inconsistency: on a given input dataset and using a given clustering algorithm, clustering outcomes and accuracy vary widely between (1) successive runs of the same toolkit, i.e., nondeterminism, and …

Don't Bite The Bait: Phishing Attack For Internet Banking (E-Banking), Ilker Kara

Journal of Digital Forensics, Security and Law

Phishing attacks are based on obtaining desired information from users quickly and easily with the help of misdirecting, panicking, curiosity, or excitement. Most of the phishing web sites are designed on internet banking(e-banking) and the attackers can acquire financial information of misled users with the tactics and discourses they develop. Despite the increase of prevention techniques against phishing attacks day by day, an effective solution could not be found for this issue due to the human factor. Because of this reason, real phishing attack studies are essential to study and analyze the attackers’ attack techniques and strategies. This study focused …

Taxthemis: Interactive Mining And Exploration Of Suspicious Tax Evasion Group, Yating Lin, Kamkwai Wong, Yong Wang, Rong Zhang, Bo Dong, Huamin Qu, Qinghua Zheng

Research Collection School Of Computing and Information Systems

Tax evasion is a serious economic problem for many countries, as it can undermine the government’s tax system and lead to an unfair business competition environment. Recent research has applied data analytics techniques to analyze and detect tax evasion behaviors of individual taxpayers. However, they have failed to support the analysis and exploration of the related party transaction tax evasion (RPTTE) behaviors (e.g., transfer pricing), where a group of taxpayers is involved. In this paper, we present TaxThemis, an interactive visual analytics system to help tax officers mine and explore suspicious tax evasion groups through analyzing heterogeneous tax-related data. A …

The Role Of Trust In Advice Acceptance From Non-Human Actors, Rahul Banerjee

Dissertations and Theses Collection (Open Access)

Advancements in technology are now allowing non-human actors in the form of robot-advisors, driverless cars, medical assistants to perform increasingly complex tasks. While technological change is as old as civilization, these non-human actors can do novel tasks. One such task is that they provide advice which is a credence service (Dulleck, & Kerschbamer, 2006). Using a financial services context this thesis studies the role trust plays in advice acceptance.

Robo-advisors are rapidly replacing human financial advisors as the agent-provider for portfolio investment services. For centuries, it was the banker (human financial advisor) who was responsible for providing his investors with …

Impact Of Information Breaches On Health Care Records, Anton Antony Arockiasamy

Walden Dissertations and Doctoral Studies

Although there were almost 3.5 million reported information breaches of health care data in the first quarter of 2019, health care providers do not know the extent of digital and nondigital breaches of patient medical records. The purpose of this quantitative, comparative study was to identify the difference between the individual patient records affected by digital versus nondigital breaches for three types of health care entities in the United States, health care providers, health care plans, and health care clearinghouses. Allman’s privacy regulation theory, the National Institute of Standards and Technology Privacy Framework, and ecological systems theory comprised the theoretical …

Can Generative Adversarial Networks Help Us Fight Financial Fraud?, Sean Mciver

Dissertations

Transactional fraud datasets exhibit extreme class imbalance. Learners cannot make accurate generalizations without sufficient data. Researchers can account for imbalance at the data level, algorithmic level or both. This paper focuses on techniques at the data level. We evaluate the evidence of the optimal technique and potential enhancements. Global fraud losses totalled more than 80 % of the UK’s GDP in 2019. The improvement of preprocessing is inherently valuable in fighting these losses. Synthetic minority oversampling technique (SMOTE) and extensions of SMOTE are currently the most common preprocessing strategies. SMOTE oversamples the minority classes by randomly generating a point between …

Security Awareness Strategies Used In The Prevention Of Cybercrimes By Cybercriminals, Pascal Pouani Tientcheu

Walden Dissertations and Doctoral Studies

Cybercrime is a growing phenomenon that impacts many lives worldwide. Businesses, organizations, and governments continue to search for ways to protect their data and intellectual property from cybercrimes. Grounded in the routine activity theory, the purpose of this general qualitative study was to explore strategies information security officers used to prevent cybercrimes. The participants included seven information security officers listed on social media who manage information security within organizations located in the northeast geographic region of the United States. Data were collected using semistructured interviews, the National Institute of Standards and Technology documentations and analyzed using thematic analysis. Four key …

Classifying Imbalanced Financial Fraud Data Utilizing Enhanced Random Forest Algorithm, Charles Gardner

Master of Science in Computer Science Theses

Imbalanced datasets have been a unique challenge for machine learning, requiring specialized approaches to correctly classify the minority class. Financial fraud detection involves using highly imbalanced datasets with a class imbalance of up to .01% frauds to 99.99% regular transactions. It is essential to identify all frauds in financial fraud detection, even if some classifications' precision is low. I developed a random forest assembly that separates fraudulent transactions into tiers of precision. With this approach, 96% of fraudulent transactions are identified, showing an 8% increase in recall when compared to standard approaches. 59% of fraud classifications' precision increases by 10% …

Adaptive Ensemble Of Classifiers With Regularization For Imbalanced Data Classification, Chen Wang, Chengyuan Deng, Zhoulu Yu, Dafeng Hui, Xiaofeng Gong, Ruisen Luo

Biology Faculty Research

The dynamic ensemble selection of classifiers is an effective approach for processing label-imbalanced data classifications. However, such a technique is prone to overfitting, owing to the lack of regularization methods and the dependence on local geometry of data. In this study, focusing on binary imbalanced data classification, a novel dynamic ensemble method, namely adaptive ensemble of classifiers with regularization (AER), is proposed, to overcome the stated limitations. The method solves the overfitting problem through a new perspective of implicit regularization. Specifically, it leverages the properties of stochastic gradient descent to obtain the solution with the minimum norm, thereby achieving regularization; …

Book Review: Computer Capers: Tales Of Electronic Thievery, Embezzlement, And Fraud. By Thomas Whiteside, Brian Nussbaum

International Journal of Cybersecurity Intelligence & Cybercrime

No abstract provided.

A Survey Of Serious Games For Cybersecurity Education And Training, Winston Anthony Hill Jr., Mesafint Fanuel, Xiaohong Yuan, Jinghua Zhang, Sajad Sajad

KSU Proceedings on Cybersecurity Education, Research and Practice

Serious games can challenge users in competitive and entertaining ways. Educators have used serious games to increase student engagement in cybersecurity education. Serious games have been developed to teach students various cybersecurity topics such as safe online behavior, threats and attacks, malware, and more. They have been used in cybersecurity training and education at different levels. Serious games have targeted different audiences such as K-12 students, undergraduate and graduate students in academic institutions, and professionals in the cybersecurity workforce. In this paper, we provide a survey of serious games used in cybersecurity education and training. We categorize these games into …

The Future Of Work Now: Ai-Driven Transaction Surveillance At Dbs Bank, Thomas H. Davenport, Steven M. Miller

Research Collection School Of Computing and Information Systems

One of the most frequently-used phrases at business events these days is “the future of work.” It’s increasingly clear that artificial intelligence and other new technologies will bring substantial changes in work tasks and business processes. But while these changes are predicted for the future, they’re already present in many organizations for many different jobs. The job and incumbents described below are an example of this phenomenon. Steve Miller of Singapore Management University and I co-authored the story.

Detecting Credit Card Fraud: An Analysis Of Fraud Detection Techniques, William Lovo

Senior Honors Projects, 2020-current

Advancements in the modern age have brought many conveniences, one of those being credit cards. Providing an individual the ability to hold their entire purchasing power in the form of pocket-sized plastic cards have made credit cards the preferred method to complete financial transactions. However, these systems are not infallible and may provide criminals and other bad actors the opportunity to abuse them. Financial institutions and their customers lose billions of dollars every year to credit card fraud. To combat this issue, fraud detection systems are deployed to discover fraudulent activity after they have occurred. Such systems rely on advanced …

A Data-Analytics Approach For Risk Evaluation In Peer-To-Peer Lending Platforms, Feng He, Yuelei Li, Tiecheng Xu, Libo Yin, Wei Zhang, Xiaotao Zhang

Research Collection School Of Accountancy

The goal of this article is to investigate the roles of individual behavior characteristics and Internet finance industry risk in the light of bank run theory for P2P. We know that risk evaluation is clearly important for peer-to-peer (P2P) lending platforms in China, as during the last two years, the industry has experienced thousands of platform crashes. Traditional approaches to evaluate enterprise risk are increasingly ineffective in this industry, due to the difficulty of assessing the real information. In addition, the Internet business model makes it possible to record new kinds of information. By applying a data-driven analytics method, we …

Drinking Water Governance For Whom? An Institutional Analysis Of Rural Drinking Water Systems In New Mexico, Tucker Colvin

Geography ETDs

Rural community drinking water systems in New Mexico are facing many challenges, including a lack of personnel, deteriorating infrastructure, lack of funds, overly burdensome and confusing regulation, environmental concerns, and concerns over water rights. Governing agencies are creating vulnerability by making managers prioritize some issues and neglect others. Water systems designated a Mutual Domestic Water Consumers Associations are especially problematic because they are small and managed by volunteers but have as much regulatory burden as larger municipalities. I use the theory of institutional work to explain how an institution that was originally designed to help low-income and rural communities is …

Teaching About The Dark Web In Criminal Justice Or Related Programs At The Community College And University Levels., Scott H. Belshaw, Brooke Nodeland, Lorrin Underwood, Alexandrea Colaiuta

Journal of Cybersecurity Education, Research and Practice

Increasingly, criminal justice practitioners have been called on to help solve breaches in cyber security. However, while the demand for criminal justice participation in cyber investigations increases daily, most universities are lagging in their educational and training opportunities for students entering the criminal justice fields. This article discusses the need to incorporate courses discussing the Dark Web in criminal justice. A review of existing cyber-criminal justice programs in Texas and nationally suggests that most community colleges and 4-year universities have yet to develop courses/programs in understanding and investigating the Dark Web on the internet. The Dark Web serves as the …

Reducing Payment-Card Fraud, Chares R. Ross

Walden Dissertations and Doctoral Studies

Critical public data in the United States are vulnerable to theft, creating severe financial and legal implications for payment-card acceptors. When security analysts and managers who work for payment card processing organizations implement strategies to reduce or eliminate payment-card fraud, they protect their organizations, consumers, and the local and national economy. Grounded in Cressey’s fraud theory, the purpose of this qualitative single case study was to explore strategies business owners and card processors use to reduce or eliminate payment-card fraud. The participants were 3 data security analysts and 1 manager working for an international payment card processing organization with 10 …

Strategies To Mitigate The Effects Of Identity Theft In The Hospitality Industry, Patricia Lee Jirsa

Walden Dissertations and Doctoral Studies

Leaders in the U.S. hospitality industry experience significant losses in profitability, increased mitigation cost, and reduced revenues because of business and consumer identity theft. Grounded in the fraud triangle theory and the fraud diamond theory, the purpose of this qualitative multiple-case study was to explore strategies leaders in the hospitality industry use to mitigate identity theft. A purposeful sample of 5 leaders of 5 different hospitality businesses in Montana participated in the study. Data were collected through semistructured interviews, member checking, and a review of company documents. During data analysis using Yin’s 5-step process, 3 key themes emerged: a new …

Effective Data Analytics And Security Strategies In Internal Audit Organizations, Desiree Auchey

Walden Dissertations and Doctoral Studies

The digitization of the corporate and regulatory environment presents an opportunity for internal audit organizations to change their audit techniques and increase their value to corporations. Audit functions have not kept pace with these advancements, as evidenced by the massive frauds in recent years, and current audit methodology does not robustly incorporate analytics and security of data. Grounded in agency theory, the purpose of this qualitative case study was to explore successful strategies business leaders use to implement data analytics and security for internal auditing and fraudulent activity. The participants comprised 3 audit leaders in Pennsylvania, who effectively used data …

Strategies To Mitigate The Effects Of Identity Theft In The Hospitality Industry, Patricia Lee Jirsa

Walden Dissertations and Doctoral Studies

Leaders in the U.S. hospitality industry experience significant losses in profitability, increased mitigation cost, and reduced revenues because of business and consumer identity theft. Grounded in the fraud triangle theory and the fraud diamond theory, the purpose of this qualitative multiple-case study was to explore strategies leaders in the hospitality industry use to mitigate identity theft. A purposeful sample of 5 leaders of 5 different hospitality businesses in Montana participated in the study. Data were collected through semistructured interviews, member checking, and a review of company documents. During data analysis using Yin’s 5-step process, 3 key themes emerged: a new …

Df 2.0: An Automated, Privacy Preserving, And Efficient Digital Forensic Framework That Leverages Machine Learning For Evidence Prediction And Privacy Evaluation, Robin Verma, Jayaprakash Govindaraj Dr, Saheb Chhabra, Gaurav Gupta

Journal of Digital Forensics, Security and Law

The current state of digital forensic investigation is continuously challenged by the rapid technological changes, the increase in the use of digital devices (both the heterogeneity and the count), and the sheer volume of data that these devices could contain. Although data privacy protection is not a performance measure, however, preventing privacy violations during the digital forensic investigation, is also a big challenge. With a perception that the completeness of investigation and the data privacy preservation are incompatible with each other, the researchers have provided solutions to address the above-stated challenges that either focus on the effectiveness of the investigation …

Analysis And Categorization Of Drive-By Download Malware Using Sandboxing And Yara Ruleset, Mohit Singhal

Computer Science and Engineering Theses - Archive

With the increase in the usage of websites as the main source of information gathering, malicious activity especially drive-by download has exponentially increased. A drive-by download refers to unintentional download of malicious code to a user computer that leaves the user open to a cyberattack. It has become the preferred distribution vector for many malware families. Malware is any software intentionally designed to cause damage to a user computer. The purpose of this research is to analyze the malware that were obtained from visiting approximately 100,000 malicious URLs and then running these binaries in sandboxes and then analyzing their runtime …

Artificial Intelligence Hits The Barrier Of Meaning, Melanie Mitchell

Computer Science Faculty Publications and Presentations

Today’s AI systems sorely lack the essence of human intelligence: Understanding the situations we experience, being able to grasp their meaning. The lack of humanlike understanding in machines is underscored by recent studies demonstrating lack of robustness of state-of-the-art deep-learning systems. Deeper networks and larger datasets alone are not likely to unlock AI’s “barrier of meaning”; instead the field will need to embrace its original roots as an interdisciplinary science of intelligence.