Digital Commons Network^™

Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta

Electronic Theses, Projects, and Dissertations

This Culminating Experience Project explores the use of machine learning algorithms to detect credit card fraud. The research questions are: Q1. What cross-domain techniques developed in other domains can be effectively adapted and applied to mitigate or eliminate credit card fraud, and how do these techniques compare in terms of fraud detection accuracy and efficiency? Q2. To what extent do synthetic data generation methods effectively mitigate the challenges posed by imbalanced datasets in credit card fraud detection, and how do these methods impact classification performance? Q3. To what extent can the combination of transfer learning and innovative data resampling techniques …

Risk Assessment Approaches In Banking Sector –A Survey, Mona Sharaf, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

Prediction analysis is a method that makes predictions based on the data currently available. Bank loans come with a lot of risks to both the bank and the borrowers. One of the most exciting and important areas of research is data mining, which aims to extract information from vast amounts of accumulated data sets. The loan process is one of the key processes for the banking industry, and this paper examines various prior studies that used data mining techniques to extract all served entities and attributes necessary for analytical purposes, categorize these attributes, and forecast the future of their business …

Unveiling The Digital Shadows: Cybersecurity And The Art Of Digital Forensics, Derek Beardall

Cyber Operations and Resilience Program Graduate Projects

This paper navigates the symbiotic relationship between cybersecurity and digital forensics, exploring the profound role of digital forensic methodologies in addressing cyber incidents. Beginning with foundational definitions and historical evolution, this study delves into diverse types of methodologies and their applications across law enforcement and cybersecurity domains. The mechanics of cyber incident response illuminates the strategic orchestration of digital forensic methodologies. Amidst triumphs, challenges emerge from the shadows: swift threat evolution, digital ecosystem complexity, standardization gaps, resource limitations, and legal intricacies. Best practices guide experts through this intricate terrain, culminating in an enhanced understanding of the inseparable bond between cybersecurity …

Accounting And Financial Statements Auto Analysis System, Zhen Jia

Electronic Theses, Projects, and Dissertations

This project was motivated by the need to revolutionize the generation of financial statements and financial analysis process thus speeding up business decision making. The research questions were: 1) How can machine learning increase the speed of financial statement preparation and automate financial statements analysis? 2) How can businesses balance the benefits of automating financial analysis with potential concerns around privacy, data security, and bias? 3) Can the Java J2EE framework provide a reliable running environment for machine learning?

The findings were: 1) Machine learning can significantly increase the accuracy and speed of financial analysis. Using machine learning algorithms, financial …

Exploring Strategies To Protect Nonprofit Organizations’ Assets From Fraud, Georjean W. Trinkle

Walden Dissertations and Doctoral Studies

Community action agencies serve low-income individuals, families, and communities. Community action agencies may be at risk of fraud if they do not have board members with the knowledge to implement effective governance strategies to protect the organization's assets from fraud. Grounded in agency theory, the purpose of this qualitative multiple case study was to explore effective governance strategies that some community action board members use to protect their organization's assets from fraud. The participants were three board members of two community action agencies in New Jersey who implemented effective governance strategies to protect the organization's assets from fraud. Data were …

Application Of Big Data Technology, Text Classification, And Azure Machine Learning For Financial Risk Management Using Data Science Methodology, Oluwaseyi A. Ijogun

College of Graduate Studies: Theses & Dissertations

Data science plays a crucial role in enabling organizations to optimize data-driven opportunities within financial risk management. It involves identifying, assessing, and mitigating risks, ultimately safeguarding investments, reducing uncertainty, ensuring regulatory compliance, enhancing decision-making, and fostering long-term sustainability. This thesis explores three facets of Data Science projects: enhancing customer understanding, fraud prevention, and predictive analysis, with the goal of improving existing tools and enabling more informed decision-making. The first project examined leveraged big data technologies, such as Hadoop and Spark, to enhance financial risk management by accurately predicting loan defaulters and their repayment likelihood. In the second project, we investigated …

Establishment And Mapping Of Heterogeneous Anomalies In Network Intrusion Datasets, Liam Riddell, Mohiuddin Ahmed, Paul Haskell-Dowland

Research outputs 2022 to 2026

Anomaly detection in the scope of network security aims to identify network instances for the unexpected and unique, with various security operations employing such techniques to facilitate effective threat detection. However, many systems have been designed based on the absolute mapping of attacks to one of three anomaly types (i.e. point, collective, or contextual), a strategy not supported by the recent findings of hybrid anomaly classifications. Given the growing usage of network anomaly detection and the implications of hybrid anomalies, we propose several heterogeneous anomaly types and provide an unsupervised approach for the automated mapping of network threats. Initial findings …

Credit Card Fraud Detection Using Machine Learning Techniques, Nermin Samy Elhusseny, Shimaa Mohamed Ouf, Amira M. Idrees Ami

Future Computing and Informatics Journal

This is a systematic literature review to reflect the previous studies that dealt with credit card fraud detection and highlight the different machine learning techniques to deal with this problem. Credit cards are now widely utilized daily. The globe has just begun to shift toward financial inclusion, with marginalized people being introduced to the financial sector. As a result of the high volume of e-commerce, there has been a significant increase in credit card fraud. One of the most important parts of today's banking sector is fraud detection. Fraud is one of the most serious concerns in terms of monetary …

Deep Learning And Feature Engineering For Human Activity Recognition: Exploiting Novel Rich Learning Representations And Sub-Transfer Learning To Boost Practical Performance, Ria Kanjilal

USF Tampa Graduate Theses and Dissertations

A significant gap exists in our knowledge of how domain-specific feature extraction compares to unsupervised feature learning in the latent space of a deep neural network for a range of temporal applications including human activity recognition. This dissertation aims to address this gap specifically for human activity recognition using acceleration data. To ensure reproducibility, we use two publicly available datasets, UniMiB-SHAR and ExtraSensory, with a well-established history in the human activity recognition literature. We methodically analyze the performance of 64 different combinations of i) learning representations (in the form of raw temporal data or extracted features), ii) traditional and modern …

Digital Forensics Range, Cody P. Shanahan, Bryson Y. Shishido, Samuel R. Mckee, Justin Siu, Lisa Li, Maxwell Brewer

Computer Engineering

The Digital Forensics Range was developed to serve as an online training for groups interested in computer forensics. This year's team had the goal to expand upon last year, by adding a new forensics image, unity scenario, and additional AWS functionality. The team still wanted to continue with last year's goals of keeping the training easily runnable, quickly deployable, and rapidly scalable through the use of the cloud. Adding to last year's work, this year's team hoped to further increase the educational value of the simulation with more practice, and the addition of feedback. The training is meant to be …

Deepfakes, Shallowfakes, And The Need For A Private Right Of Action, Eric Kocsis

Dickinson Law Review (2017-Present)

For nearly as long as there have been photographs and videos, people have been editing and manipulating them to make them appear to be something they are not. Usually edited or manipulated photographs are relatively easy to detect, but those days are numbered. Technology has no morality; as it advances, so do the ways it can be misused. The lack of morality is no clearer than with deepfake technology.

People create deepfakes by inputting data sets, most often pictures or videos into a computer. A series of neural networks attempt to mimic the original data set until they are nearly …

Leveraging Maching Learning In Financial Fraud Forensics In The Age Of Cybersecurity, Md. Ariful Haque, Sachin Shetty

VMASC Publications

Financial sectors are lucrative cyber-attack targets because of their immediate financial gain. As a result, financial institutions face challenges in developing systems that can automatically identify security breaches and separate fraudulent transactions from legitimate transactions. Today, organizations widely use machine learning techniques to identify any fraudulent behavior in customers' transactions. However, machine learning techniques are often challenging because of financial institutions' confidentiality policy, leading to not sharing the customer transaction data. This chapter discusses some crucial challenges of handling cybersecurity and fraud in the financial industry and building machine learning-based models to address those challenges. The authors utilize an open-source …

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 …

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 …

An Improvement Of Data Analytics Detection Rules In The Internal Audit Of The Procurement And Inventory Processes, Penpak Rangsipunyaporn

Chulalongkorn University Theses and Dissertations (Chula ETD)

Data analytics is a powerful tool to deliver value-added internal audit results as it reduces execution time and increases efficiency, effectiveness, and assurance level compared with the audit sampling approach. Because detection rules are criteria applied in the data analytics method to discover anomalies in the business transactions. Therefore, this project aims to improve the anomaly detection rules in the procure-to-pay and inbound inventory processes for an audit firm based in Thailand to provide internal audit services for clients in the manufacturing business. The sub-processes cover (1) process governance, (2) vendor selection, evaluation, and master data maintenance, (3) ordering, (4) …

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 …

Machine Learning Techniques For Credit Card Fraud Detection, Hossam Eldin Mohammed Abd El-Hamid Ahmed Abdou, Wael Khalifa, Mohamed Ismail Roushdy, Abdel-Badeeh M. Salem

Future Computing and Informatics Journal

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they …

Assessing The Prevalence Of Suspicious Activities In Asphalt Pavement Construction Using Algorithmic Logics And Machine Learning, Mostofa Najmus Sakib

Boise State University Theses and Dissertations

Quality Control (QC) and Quality Assurance (QA) is a planned systematic approach to secure the satisfactory performance of Hot mix asphalt (HMA) construction projects. Millions of dollars are invested by government and state highway agencies to construct large-scale HMA construction projects. QC/QA is statistical approach for checking the desired construction properties through independent testing. The practice of QC/QA has been encouraged by the Federal Highway Administration (FHWA) since the mid 60’s. However, the standard QC/QA practice is often criticized on how effective such statistical tests and how representative the reported material tests are. Material testing data alteration in the HMA …

Intrusion Detection For Cyber-Physical Attacks In Cyber-Manufacturing System, Mingtao Wu

Dissertations - ALL

In the vision of Cyber-Manufacturing System (CMS) , the physical components such as products, machines, and tools are connected, identifiable and can communicate via the industrial network and the Internet. This integration of connectivity enables manufacturing systems access to computational resources, such as cloud computing, digital twin, and blockchain. The connected manufacturing systems are expected to be more efficient, sustainable and cost-effective.

However, the extensive connectivity also increases the vulnerability of physical components. The attack surface of a connected manufacturing environment is greatly enlarged. Machines, products and tools could be targeted by cyber-physical attacks via the network. Among many emerging …

Bleeding Out: The Case For Strengthening Healthcare Client Portal Data Privacy Regulations, Matthew D. Mccord

Minnesota Journal of Law, Science & Technology

No abstract provided.

Detection Of Fraud Risks In Retailing Sector Using Mlp And Svm Techniques, Davut Pehli̇vanli, Süleyman Eken, Ebu Beki̇r Ayan

Turkish Journal of Electrical Engineering and Computer Sciences

In today's business conditions, where business activities are spreading over a wide geographical area, fraud auditing processes have crucial importance especially for the retailing sector which has a high branch network. In the retailing sector, especially purchasing processes are subject to high fraud risks. This paper shows that it is possible to detect fraudulent processes by applying data mining techniques on operational data related to purchasing activities. Within this scope, in order to detect the fraudulent purchasing operations, support vector machine (SVM) models with different kernels and artificial neural networks methods have been used and successful results have been achieved. …

Scheduling In Mapreduce Clusters, Chen He

School of Computing: Dissertations, Theses, and Student Research

MapReduce is a framework proposed by Google for processing huge amounts of data in a distributed environment. The simplicity of the programming model and the fault-tolerance feature of the framework make it very popular in Big Data processing.

As MapReduce clusters get popular, their scheduling becomes increasingly important. On one hand, many MapReduce applications have high performance requirements, for example, on response time and/or throughput. On the other hand, with the increasing size of MapReduce clusters, the energy-efficient scheduling of MapReduce clusters becomes inevitable. These scheduling challenges, however, have not been systematically studied.

The objective of this dissertation is to …

Application Of Synthetic Informative Minority Over-Sampling (Simo) Algorithm Leveraging Support Vector Machine (Svm) On Small Datasets With Class Imbalance, Akshatha Fakkeriah Kallappanamatt

Dissertations

Developing predictive models for classification problems considering imbalanced datasets is one of the basic difficulties in data mining and decision-analytics. A classifier’s performance will decline dramatically when applied to an imbalanced dataset. Standard classifiers such as logistic regression, Support Vector Machine (SVM) are appropriate for balanced training sets whereas provides suboptimal classification results when used on unbalanced dataset. Performance metric with prediction accuracy encourages a bias towards the majority class, while the rare instances remain unknown though the model contributes a high overall precision. There are chances where minority instances might be treated as noise and vice versa. (Haixiang et …

Ensemble Methods For Anomaly Detection, Zhiruo Zhao

Dissertations - ALL

Anomaly detection has many applications in numerous areas such as intrusion detection, fraud detection, and medical diagnosis. Most current techniques are specialized for detecting one type of anomaly, and work well on specific domains and when the data satisfies specific assumptions.

We address this problem, proposing ensemble anomaly detection techniques that perform well in many applications, with four major contributions: using bootstrapping to better detect anomalies on multiple subsamples, sequential application of diverse detection

algorithms, a novel adaptive sampling and learning algorithm in which the anomalies are iteratively examined, and improving the random forest algorithms for detecting anomalies in streaming …

Protecting Non-Public Information In Community Banks: A Study For Implementing Risk Based Framework For Protecting Non Public Personal Information, Bruce Lee Upton

Theses and Dissertations

In today’s world it seems that cyber hackers, cyber terrorists, and cyber activists make headlines almost every day. Information security has become a major concern for bankers from both a reputation and regulatory examination standpoint. Bank management is tasked to answer a litany of questions. Is my bank secure? What impact does this security have on productivity? What solutions are cost effective? What impact will a security breach have on my bank and respective customer base? Additionally, many bankers don’t have on-staff expertise to answer these questions. Having a framework where bankers can know how likely it is they will …

Cyber Security Risks In Public High Schools, Ion Goran

Student Theses

Today, just like other organizations, schools are vulnerable to cyber-attacks. This vulnerability has vividly revealed itself in recent years, with the number of attacks on public schools increasing and taking ever-changing forms. Today, the student’s grades, disciplinary notes, learning diagnoses, phone numbers, addresses, and another identifying information is all at risk of being exposed. Moreover, poor network security poses a dire threat to parents of school children whose personal records contain sensitive or dangerous information. The practical implications of these attacks require intervention or remedy to increase cyber security. Cyberattacks may take place when storage facilities or infected devices are …

Can Phishing Education Enable Users To Recognize Phishing Attacks?, Hanaa Alghamdi

Dissertations

Phishing attacks have increased rapidly and caused many drastic damages and losses for internet users‟ .The purpose of this research is to investigate on effectiveness of phishing education and training to help users identify different forms of phishing threats. The study has been conducted through developing a phishing quiz mobile application which includes four kinds of phishing threats. It tested the ability of users to recognize spoofed emails, SMS phishing (SMshing), scam phone calls (Vishing), and phishing through social media networks. A comprehensive literature review was discussed to investigate on the research area, understand the research problem, support the proposed …

A Reduced Labeled Samples (Rls) Framework For Classification Of Imbalanced Concept-Drifting Streaming Data., Elaheh Arabmakki

Electronic Theses and Dissertations

Stream processing frameworks are designed to process the streaming data that arrives in time. An example of such data is stream of emails that a user receives every day. Most of the real world data streams are also imbalanced as is in the stream of emails, which contains few spam emails compared to a lot of legitimate emails. The classification of the imbalanced data stream is challenging due to the several reasons: First of all, data streams are huge and they can not be stored in the memory for one time processing. Second, if the data is imbalanced, the accuracy …

Paper Session Ii: Computer Forensics Field Triage Process Model, Marcus K. Rogers, James Goldman, Rick Mislan, Timothy Wedge, Steve Debrota

Annual ADFSL Conference on Digital Forensics, Security and Law

With the proliferation of digital based evidence, the need for the timely identification, analysis and interpretation of digital evidence is becoming more crucial. In many investigations critical information is required while at the scene or within a short period of time - measured in hours as opposed to days. The traditional cyber forensics approach of seizing a system(s)/media, transporting it to the lab, making a forensic image(s), and then searching the entire system for potential evidence, is no longer appropriate in some circumstances. In cases such as child abductions, pedophiles, missing or exploited persons, time is of the essence. In …

Designing A Data Warehouse For Cyber Crimes, Il-Yeol Song, John D. Maguire, Ki Jung Lee, Namyoun Choi, Xiaohua Hu, Peter Chen

Annual ADFSL Conference on Digital Forensics, Security and Law

One of the greatest challenges facing modern society is the rising tide of cyber crimes. These crimes, since they rarely fit the model of conventional crimes, are difficult to investigate, hard to analyze, and difficult to prosecute. Collecting data in a unified framework is a mandatory step that will assist the investigator in sorting through the mountains of data. In this paper, we explore designing a dimensional model for a data warehouse that can be used in analyzing cyber crime data. We also present some interesting queries and the types of cyber crime analyses that can be performed based on …