Digital Commons Network^™

A Study On Online Romance Fraud And National Security: A Ghanaian Perspective, Osmond B. Kabah

Doctoral Dissertations and Projects

One of the most prevalent cybercrimes is online romance fraud, where the perpetrators prey on individuals looking for love on various platforms. The crime, widespread on dating apps and social media, is typified by scammers starting a relationship and then defrauding the gullible victims of their money. Ghana is one of the countries whose citizens have been victims of these swindlers, and the national security apparatus is increasingly receiving reports of online financial victimization. This qualitative study aims to explore the issue in the country from a national security perspective. This study investigates what motivates the perpetrators of this crime …

Denaturalization's Missing Limit, Cassandra Burke Robertson, Irina D. Manta

NULR Online

No abstract provided.

Artificial Intelligence (Ai) In Forensic Psychology: An Umbrella Review Of Potentials And Pitfalls, Ysabel Thereze Ang Guevarra, Nur Eva Alisha Binte Mohamed Hisham, Andree Hartanto

Research Collection School of Social Sciences

Artificial intelligence (AI) is becoming increasingly embedded within forensic psychological practice, shaping how criminal risk, legal responsibility and public safety are assessed. AI tools are now used in recidivism prediction, behavioural analysis, deception detection and investigative support, high-stakes domains where errors can have profound consequences. Despite this rapid adoption, the existing literature remains fragmented, with most reviews confined to narrow subdomains and offering limited integrated synthesis of AI′s broader role in forensic psychology. Thus, this umbrella review addresses this gap by synthesising findings from 43 reviews obtained from five major databases, namely EBSCOhost ERIC, EBSCOhost PsycInfo, PubMed, Scopus and Web …

The Role Of Online Disinhibition On Social Media Users' Privacy Concerns And Behaviors, Lisa Thompson, Sinyong Choi

Faculty Articles

As social media platforms become central to digital interactions, concerns about both privacy and the disclosure of personal information have evolved. Online disinhibition—the psychological detachment users experience in digital spaces that distance their online behaviors from offline consequences—may shape perceptions of privacy risks, particularly on social media platforms like TikTok. This study examines the relationships between online disinhibition and users’ privacy concerns and protective behaviors while accounting for individuals’ sense of gratification, media awareness, identity, and experience. By exploring dimensions such as anonymity and invisibility, we investigate how these factors are linked to self-disclosure and diminished privacy caution. Findings from …

Method For Trust-Aware And Language Model–Driven Email Threat Detection And Mitigation, Niranjan M M

Defensive Publications Series

Email remains the primary communication channel for enterprises and continues to be the most exploited attack vector for cyber threats. Modern email attacks increasingly rely on impersonation, social engineering, and contextual manipulation rather than traditional malware or malicious links, allowing them to evade existing detection mechanisms. At the same time, email security systems are adopting large language models (LLMs) to improve intent detection and contextual analysis, introducing new risks where email content itself can manipulate or degrade automated reasoning.

The proposal introduces a secure, trust-aware, and LLM-safe framework for automated email threat detection and remediation. The proposed system integrates sender …

Impartial Intelligence? Evidence Of Country-Label Sensitivity In Ai Financial Analysis, Fabio Motoki, Jedson Pinto

School of Accountancy Faculty Publications

This study examines whether large language models exhibit systematic country-contingent differential treatment in financial fraud detection. Analyzing 30,000 synthetic transactions with identical statistical properties across three country attributions (United States, Great Britain, and China), we find LLMs assign significantly higher fraud probabilities to Chinese-attributed transactions (36.2%) compared to Western countries (≈30–31%), resulting in accuracy disparities of 67% versus 74%. The gap remains stable across five independent experimental replications and persists when using Chinese language prompts, ruling out linguistic effects. Bias mitigation strategies, such as requiring explanations or explicit country neutrality instructions, reduce but fail to eliminate these disparities. Testing across …

Deep Learning For Video Anomaly Detection: A Review, Peng Wu, Chengyu Pan, Yuting Yan, Guansong Pang, Qingsen Yan, Peng Wang, Yanning Zhang

Research Collection School Of Computing and Information Systems

Video anomaly detection (VAD) aims to discover behaviors or events deviating from the normality in videos. As a long-standing task in the field of computer vision, VAD has witnessed much good progress. In the era of deep learning, with the explosion of architectures of continuously growing capability and capacity, a great variety of deep learning-based methods are constantly emerging for the VAD task, greatly improving the generalization ability of detection algorithms and broadening the application scenarios. Therefore, such a multitude of methods and a large body of literature make a comprehensive survey a pressing necessity. In this article, we present …

Techniques For Preventing Anchoring Bias In Ai Investigative Analysis Through Staged Data Redaction And Hypothesis-Gated Revelation, Avneet Singh Chhabra

Defensive Publications Series

Presented herein is a system for preventing anchoring bias in large language model (LLM) investigative analysis. Bias in LLMs is not typically addressed by instruction-level approaches; rather, it is most effectively mitigated by physically separating the biasing data from an LLM's input during the hypothesis formation phase. The proposed system includes five core components: a Data Redaction Engine, a Staged Analysis Protocol, a Structural Gate, a Corrections Bridge, and a Procedural Attestation Record. The Data Redaction Engine operates to separate source data into a redacted evidence set and an attribution set, validated by a multi-layer validation pass. The Staged Analysis …

Adaptive Outlier Detection Over Data Stream, Rui Zhu, Mingyuan Jiang, Xiaochun Yang, Baihua Zheng, Bin Wang, Tao Qiu

Research Collection School Of Computing and Information Systems

Continuous distance-based outlier detection in streaming data poses significant challenges and has a wide range of practical applications. Traditional threshold-based methods perform well under stable streaming conditions, where fixed parameters remain effective. However, they often struggle with dynamic data distributions and high stream speeds, leading to suboptimal performance, limited control over the number of returned outliers, and failure to meet real-time detection requirements. To address these issues, this paper introduces a novel Recall and Proportion-Aware Outlier Detection (RPA-OD) query. In RPA-OD, ρ defines a distance relaxation that enables real-time outlier detection. Specifically, objects with fewer than k neighbors within the …

Real-Time Fraud Detection, Haidi Aly Fahmy, Abdelhadi Nait-Zerrad, Shiva Krishana Reddy Ravuula, Sangwhan Cha

Harrisburg University Other Works

Financial fraud detection is a high-volume, high-velocity analytics problem. Traditional rule-based systems are often easy to deploy, but they are limited by static thresholds, delayed response, high false-positive rates, and weak explainability. This report presents a formalized end-to-end Big Data architecture for real-time fraud and anomaly detection in financial transaction streams.

The proposed architecture ingests transaction events through AWS Kinesis, enriches them through an Apache Flink stream-processing layer, scores them with an XGBoost classifier, explains model outputs using SHAP, and converts structured evidence into human-readable summaries through a controlled GPT explanation layer. Results are persisted through a hybrid storage strategy …

The Paradox Of Perfection: Hidden Risks Of High-Performing Ai In Human-In-The-Loop Governance, Chen Zhong, Alper Yayla, Xueping Liang

Proceedings of the Cognitive Innovation Conference (CognoCon)

As AI systems achieve near-perfect accuracy in high-stakes decision-making domains, governance frameworks risk overlooking a critical paradox: superior AI reliability may render human oversight ceremonial rather than functional. This paper presents a theoretically grounded experimental research design to investigate Automation Complacency—the cognitive degradation of human vigilance triggered by sustained streaks of accurate AI outputs. Grounded in Dual-Process Theory and the competency trap literature, we hypothesize that consistent AI reliability shifts operators from analytical (System 2) to heuristic (System 1) processing, significantly impairing error detection when AI eventually fails. Using a simulated phishing detection task (N=200), we plan to test how …

Privacy-Preserving Local Authorization For Regulated, Safety-Critical, And Financial Systems, Jonathan Stephen Baker

Defensive Publications Series

This disclosure describes systems and methods for controlling access to regulated, age-restricted, safety-critical, high-liability, and financial devices using privacy-preserving local authorization. In disclosed embodiments, a user device, terminal, embedded controller, or local authorization module verifies that a requesting user satisfies one or more access conditions before enabling operation of a protected physical or digital system. The authorization process may be performed locally or at the edge, without requiring persistent storage of raw biometric data, identity templates, or reusable personal credentials by a relying party. The system outputs a limited authorization result, proof, token, permission state, or access decision indicating that …

The Weaponization Of Transnational Crime: A Triangular Analytical Model For Hybrid Threat Development In U.S.–China–Russia Relations, 2000–2025, Michael J. Haak

Senior Honors Theses

This thesis examines the evolving strategic relationship between the United States, China, and Russia from 2000 to 2025 through a triangular model based on reactions to systemic pressures to core strategic interests. The study employs a thematic qualitative data analysis on U.S., Chinese, and Russian national security strategies to analyze strategic posturing between poles over time. This new model serves as the study’s basis for contextualizing hybrid threat development, specifically transnational crime as a proxy force of asymmetric power projection. The study found that the evolution of state-supported transnational crime serves as a by-product of reactions to systemic pressure. State …

You Can’T Spell Audit Without Ai: The Current Uses Of Artificial Intelligence In Audit, Jena Perkins

Senior Honors Theses

The accounting profession continuously adapts to the innovations provided by the broader context in which it exists. Artificial intelligence (AI) is a forerunner among tools used to enhance and optimize auditing services within the accounting profession. The realm of AI offers advancements to procedures used within an audit to detect misstatements. Based on the proprietary platforms developed by Big 4 accounting firms, AI is a key component in maintaining an advanced approach towards auditing.

Neal F. Newman, Neal F. Newman

Faculty CVs

No abstract provided.

Capitalizing On Crisis: A Statistical Analysis Of Federal Funding And The Surge In White-Collar Criminal Activity During The Covid-19 Pandemic, Lee R. Gibson

Doctoral Dissertations and Projects

The COVID-19 pandemic ushered in a new era that has forever changed how people work, communicate, and socialize. The pandemic also brought one of the most significant influxes of financial assistance to individuals, businesses, educational institutions, and state and local governments that has ever been seen. This massive infusion of cash, which was orchestrated and managed by the federal government, was instituted in a manner that provided quick relief with little ability for oversight, resulting in every assistance program experiencing high rates of fraudulent activity. This project aims to understand if the levels of white-collar crime increase during a disaster, …

Changes In Internet Search Term Popularity In Elder Mistreatment (2018-2023): Infodemiology Study Of Google Trends Data, Nicholas Sevey, Melvin Livingston, Kristin Lees-Haggerty, Olanike Ojelabi, Randi Campetti, Jason Burnett, Carolyn E Z Pickering, David Hancock, Rachit Sabharwal, Michael Brad Cannell

Faculty, Staff and Student Publications

Background: Elder mistreatment (EM) is a significant public health problem that is frequently underdetected and underreported. Insufficient public recognition and engagement have been hypothesized as one contributor to this underreporting; however, few data sources exist to quantify public awareness or engagement with EM at the population level.

Objective: This study examined relative internet search interest in EM compared with other forms of abuse (child abuse and domestic violence) in the United States using Google Trends data.

Methods: We analyzed Google Trends data to compare the Relative Search Index (RSI) for the terms "elder abuse," "child abuse," and "domestic violence" in …

Private Coverage, Public Risk: The Role Of Cyber Insurance In National Security Governance, Deborah L. Johnson

Faculty Publications

This Article explores the growing yet underexamined role of the commercial cyber insurance market in shaping the United States' approach to cybersecurity readiness. It argues that, as cyber threats against U.S. critical infrastructure and essential services increase, commercial cyber insurance has quietly come to function as a de facto governance tool in national cybersecurity. In hospitals, water and energy systems, and other public-facing sectors, policy language, underwriting questionnaires, and post-breach claims practices help define what counts as "reasonable" security, influence how organizations plan for and respond to incidents, and affect the pace and scope of recovery after an attack. Yet …

Adolph Michelin V. Warden Moshannon Valley Correctional Center

2026 Decisions

USDC for the Western District of Pennsylvania

Deep Learning Approaches For Anti-Money Laundering On Mobile Transactions: Review, Framework, And Directions, Jiani Fan, Lwin Khin Shar, Ruichen Zhang, Ziyao Liu, Wenzhuo Yang, Dusit Niyato, Kwok-Yan Lam

Research Collection School Of Computing and Information Systems

Money laundering is a financial crime that obscures the origin of illicit funds, necessitating the development and enforcement of anti-money laundering (AML) policies by governments and organizations. The proliferation of mobile payment platforms and smart IoT devices has significantly complicated AML investigations. As payment networks become more interconnected, there is an increasing need for efficient real-time detection to process large volumes of transaction data on heterogeneous payment systems by different operators such as digital currencies, cryptocurrencies, and account-based payments. Most of these mobile payment networks are supported by connected devices, many of which are considered loT devices in the FinTech …

Improving Credit Card Transaction Fraud Detection Using Cvqboosting, Bethel Hui Ting Loke, Nirvik Sahoo, Bingyan Guan, Minrui Xu, Dev Verma, Paul R. Griffin

Research Collection School Of Computing and Information Systems

This paper introduces a novel hybrid quantum-classical approach to credit card fraud detection using CVQBoost, a hybrid quantum-classical boosting algorithm executed on the photonic Dirac-3 processor from Quantum Computing Inc. (QCi). By integrating a diverse set of weak classifiers, which includes K-nearest neighbours (KNN), linear discriminant analysis, logistic regression, and XGBoost, within a hybrid quantum-classical ensemble, the proposed method demonstrates significant improvements over the latest published classical benchmarks. Experiments on a Kaggle credit card fraud dataset show that the quantum-enhanced model achieves a mean AUC-PR score of over 0.8, corresponding to an approximately 9% relative improvement over the best published …

Graph Convolution Neural Network And Deep Q-Network Optimization-Based Intrusion Detection With Explainability Analysis, Kelvin Mwiga, Mussa Dida, Leandros Maglaras, Ahmad Mohsin, Helge Janicke, Iqbal H. Sarker

Research outputs 2022 to 2026

As networks expand in size and complexity, coupled with an exponential increase in intrusions on network and IoT systems, this leads to traditional models failing to capture increasingly intricate correlations among network components accurately. Graph Convolution Networks (GCNs) have recently acquired prominence for their capacity to represent nodes, edges, or entire graphs by aggregating information from adjacent nodes. However, the correlations between nodes and their neighbours, as well as related edges, differ. Assigning higher weights to nodes and edges with high similarity improves model accuracy and expressiveness. In this paper, we propose the GCN-DQN model, which integrates GCN with a …

National Ai Forensic Grid: Federated Multi-Modal Deepfake Detection And Cryptographic Evidence Infrastructure For Law Enforcement, Pranav Bhatnagar Mr

Defensive Publications Series

This disclosure presents a federated forensic infrastructure designed for detection of malicious synthetic media and preservation of evidentiary integrity across law enforcement jurisdictions. The system integrates multi-modal deepfake detection, cross-node artifact intelligence exchange, and cryptographic chain-of-custody preservation. The disclosed architecture enables scalable national coordination, standardized forensic scoring, and tamper-resistant evidence management suitable for judicial proceedings.

Ai Role-Play Interview Simulation In A Graduate-Level Research Methods Course, Julie Siddique

Teaching Repository of AI-Infused Learning

To enhance qualitative research skills and deepen student engagement, I introduced an AI-driven role-play strategy in a graduate-level research methods course. Students were asked to practice interview skills using an AI bot that responded as a simulated persona, allowing students to practice semi-structured interviewing and navigate ethical considerations in a simulated context.

Quantum Chebyshev Transform-Based Graph Neural Networks For Financial Fraud Detection, Minrui Xu, Bingyan Guan, Bethel Hui Ting Loke, Paul R. Griffin

Research Collection School Of Computing and Information Systems

Financial fraud detection is a critical challenge requiring accurate identification of anomalous patterns in complex transaction networks. Graph Neural Networks (GNNs) have emerged as powerful tools for fraud detection by capturing relational structures among entities. Meanwhile, quantum computing offers new possibilities to enhance machine learning through high-dimensional Hilbert spaces and parallelism. In this paper, we propose a hybrid classical-quantum model called QCTGNN (Quantum Chebyshev Transform-based Graph Neural Network) for financial fraud detection. The QCTGNN integrates a classical graph neural network component based on Simplified Graph Convolutions (SGConv) with a quantum component that performs a Chebyshev polynomial-based transform via variational quantum …

Assessing The Efficacy Of Homeland Security Task Forces (Hstf) Per Executive Order 14159, Robert J. Hehl, Stella Difronzo, Maribel Ramirez, Treyson Carino, Samuel Zwiener

CJC 450 - Criminal Justice and Criminology Capstone

This Capstone Research Assessment (CRA) will assess the efficacy of Homeland Security Task Force (HSTF) operations in Rhode Island as mandated under Presidential Executive Order 14159 (January 20, 2025), Section 6(b), Protecting the American People Against Invasion. Through review of national data, interviews of HSTF Rhode Island partner agency members, government and peer reviewed journals, and open-source reporting, this assessment will examine the efficacy of the HSTF mission including interagency coordination, resource allocation, and enforcement effectiveness. Furthermore, this CRA will assess the extent to which HSTF units have achieved intended objectives and identify operational and environmental limitations impacting overall success. …

Prosocial Ceos And Accounting Manipulation, Mei Feng, Weili Ge, Zhejia Ling, Wei Ting Loh

Research Collection School Of Accountancy

This paper examines the association between chief executive officers’ (CEOs’) prosocial tendency and their firms’ likelihood of accounting manipulation. We measure CEOs’ prosocial tendency based on their involvement with charitable organizations. We find that prosocial CEOs are less likely to engage in accounting manipulation, as proxied by material non-reliance restatements and SEC or DOJ enforcement actions. The effect is more pronounced when CEOs are involved with charities that directly aim to improve the welfare of others and when they face stronger incentives to misreport. These results continue to hold in analyses of changes in CEOs’ prosocial tendency around turnover events. …

Abuse Of Contract: A Proposal For A New Cause Of Action, Miriam A. Cherry

Faculty Publications

With the growth of online commerce and the platform economy, many companies are including provisions in their online terms and conditions that extend far beyond what reasonable consumers would expect. Some terms and conditions purport to bind customers to separate contracts in future transactions that have little to do with the first contract. Other boilerplate purports to cover family members of the customer who created an account. Some retailers have argued that people shopping in their brick-and-mortar stores are subject to terms and conditions because those shoppers had at some point previously created an online account. For example, Disney argued …

The Nature Of Domestic Violence Threats Against Federal Law Enforcement Resulting From Immigration Enforcement Efforts, Robert J. Hehl, Riley Fullam, Reagan Moffatt, Phillip Pires

CJC 450 - Criminal Justice and Criminology Capstone

This Capstone Research Assessment (CRA) focuses on the nature of the domestic violence threats against federal law enforcement resulting from enhanced immigration enforcement efforts. Through review of joint law enforcement immigration actions, and subsequent public responses in the form of violent protests and targeting of immigration and task force officers and their agencies, this CRA will identify specific threats and trends that may endanger federal law enforcement officials. This assessment will conclude with recommendations to mitigate identified threats in ensuring officer safety.

The Evolution Of Crime: Exploitation Of Technological Overdependencies Within Economic Enterprises, Robert J. Hehl, Grey Quigley, Autumn Laurenzano-Frost, Richard Mcloughlin, Valerie Garzon

CJC 450 - Criminal Justice and Criminology Capstone

This Capstone Research Assessment focuses on analyzing the evolution of crime from physical to technology-based offenses. This project examines how criminal actors exploit organizations’ increasing dependence on technology and digital systems and the inherent vulnerabilities found within economic enterprises. Said research assesses the lagging development of laws and cybersecurity safeguards, the critical issue of human error, and the lowered barrier to entry for cybercriminals. This paper will conclude with recommendations and strategies that may mitigate such vulnerabilities.