Digital Commons Network^™

Investigations Of Secure Memory For Vlsi Based Crypto System, Vijay Sai R Mr

Theses and Dissertations

Semiconductor technology is growing very rapidly in their architectural developments, involving the usage of processor and memory. Presence of memory, in general is a vital commodity in various devices which are almost embedded into human activity, from robust work stations to handy mobile phones. Security of data stored in memory is very important, and hence, observation must be made that these valuable data should not be thwarted by malicious means. Security in cache memory is a major issue in memory related applications such as smart cards and bio-metric implementations.

Cache, is a small and limited memory located between central processing …

Enhancing Fraud Detection: A Comprehensive Analysis Of Financial Transactions Using Data Visualization, Statistical Models, And Machine Learning Techniques, Juliana Patrone

Master’s Theses and Projects

No abstract provided.

Revisiting Android Permission Evolution Through Temporal Analysis: A Data-Driven Study Of Behaviors And Implications, Ali Al Kinoon

Graduate Thesis and Dissertation post-2024

Malicious applications continue to pose significant privacy and security risks within the Android ecosystem, often exploiting user permissions and obscuring data collection practices behind opaque privacy policies. To address these challenges, this dissertation presents a comprehensive framework for enhancing Android app security through dataset construction, permission behavior analysis, and privacy policy classification. The framework systematically investigates key issues such as dataset fidelity, permission misuse, model performance, and the alignment between declared and actual data practices. Through a combination of empirical analysis and machine learning techniques, this work advances the development of more transparent and secure mobile applications. The first part …

Gnsi Decision Brief: Building Trust In Digital Response: The Role Of Chatbots In Cybercrime Prevention Open-Source Tools For Safer Digital Reporting And Public Trust, George Burruss, Loni Hagen, Ly Dinh, Lingyao Li

GNSI Decision Briefs

Cybercrime poses a growing global threat, inflicting financial harm on individuals, businesses, and governments. In 2024 alone, the FBI's Internet Crime Complain Center (IC3) received over 800,000 complains, with reported losses exceeding $16.6 billion, a 33 percent increase from the prior year. The damage extends beyond financial loss: cybercrime undermines trust in digital systems and can cause psychological harm. Its anonymous and complex nature makes detection and prosecurtion difficult, creating low-risk, high-reward conditions for offenders.

The Role Of Data Analytics In Detecting Unemployment Insurance Fraud: A Case Study Of State Governments In The United States, Tina Louise Carkhuff

Doctoral Dissertations and Projects

The unprecedented surge in unemployment insurance claims during the COVID-19 pandemic exposed state labor agency systems in the United States to significant fraud risks, resulting in billions of dollars in improper payments. This study investigated the role of data analytics in detecting and mitigating unemployment insurance fraud, with a focus on state government responses. Using a single-case study approach, I examined how advanced data analytics, including machine learning, predictive modeling, and strategies to identify fraudulent claims, can reduce fraud within unemployment insurance systems. The study also included an investigation of systemic vulnerabilities and the impact of policy improvements on fraud …

Turning The Tables With Tech: Scambaiting Tools And Their Applications, Doetri Ghosh, Tatiana Renae Ringenberg

Discovery Undergraduate Interdisciplinary Research Internship

Online financial scams have become increasingly complex, utilizing fake social media accounts, remote access tools, deepfake recordings, and other advanced scamming tactics. In response, a growing community of scambaiters have matched this technological sophistication, in their efforts to disrupt the scam ecosystem. Various tools are used throughout scambaiting sessions, where proper use of technology is essential for success. Limited research exists on the changing needs and tools involved in scambaiting. This study bridges this gap by conducting an exploratory qualitative analyses of 250 posts from the Reddit group r/Scambait. We explore various aspects of technology use, specifically looking into recommendations …

Examining The Active Ingredients In An Anti-Stigma Intervention For Individuals With A Criminal History, Kaylee Cook

Electronic Theses and Dissertations Archive

Obtaining employment is key to successful reintegration for people who have been incarcerated. However, employers are often hesitant to hire formerly incarcerated persons due to general negative attitudes toward them and concern that they will not exhibit positive work behaviors. Interventions to reduce general types of stigma exist, with primary strategies involving education and/or contact. Most research examining the effectiveness of stigma-reducing strategies largely focus on reducing identity-based stigma (e.g., race, mental illness), with limited research on reducing choice-based stigma, such as those with criminal histories. However, regardless of targeted stigma, there is no consensus regarding which intervention format (i.e., …

Anomalygfm: Graph Foundation Model For Zero/Few-Shot Anomaly Detection, Hezhe Qiao, Chaoxi Niu, Ling Chen, Guansong Pang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) aims to identify abnormal nodes that differ from the majority of the nodes in a graph, which has been attracting significant attention in recent years. Existing generalist graph models have achieved remarkable success in different graph tasks but struggle to generalize to the GAD task. This limitation arises from their difficulty in learning generalized knowledge for capturing the inherently infrequent, irregular and heterogeneous abnormality patterns in graphs from different domains. To address this challenge, we propose AnomalyGFM, a GAD-oriented graph foundation model that supports zero-shot inference and few-shot prompt tuning for GAD in diverse graph datasets. …

Affinitytune: A Prompt-Tuning Framework For Few-Shot Anomaly Detection On Graphs, Jingyan Chen, Guanghui Zhu, Guansong Pang, Chunfeng Yuan, Yihua Huang

Research Collection School Of Computing and Information Systems

Graph anomaly detection (GAD) is a critical task with applications in domains such as networking, finance, and bioinformatics. % However, the scarcity of labeled anomalies and the limitations of unsupervised methods hinder effective detection. % While semi-supervised and few-shot learning approaches offer improvements, they struggle with knowledge transfer and rely heavily on labeled data. % Recent advancements in prompt tuning on graphs provide a promising direction, but their application to heterophilous graphs in anomaly detection remains underexplored. % In this work, we propose AffinityTune, a novel framework for few-shot graph anomaly detection based on prompt tuning. % Our approach introduces …

Enhancing Urban Governance For Inclusive Growth And Sustainable Development, Asti Amelia Novita

Journal of Environmental Science and Sustainable Development

In light of global challenges such as climate change, inequality, and governance shortcomings, cities have a vital part to play in achieving sustainable development. Numerous local governance systems are deficient in transparency, public participation, and social fairness mechanisms essential for implementing sustainability. This study investigates how strengthening transparency, public engagement, and social fairness can enhance Malang City’s capacity to achieve the Sustainable Development Goals (SDGs). This study employed a mixed-methods approach, using the Sustainable Value Mapping and Analysis (SVMA) framework to assess insight from 250 stakeholders, including local officials, civil society groups, and community representatives. Findings reveal modest but statistically …

The Collection Problem: How The Circuit Split On Pleading Standards In Securities Fraud Claims Undermines Federal Regulatory Goals, Lucas Immer

St. John's Law Review

(Excerpt)

The Great Depression is generally recognized as the greatest economic calamity in United States history. One of the Great Depression’s many causes was reckless financial speculation driven in part by financial fraud. In response to the crisis, Congress passed the 1934 Securities Exchange Act (“the Exchange Act”), which courts have long held creates a private right of action for plaintiffs who experience an economic loss due to reliance on a material misstatement surrounding the purchase or sale of a security. A prima facie claim for securities fraud under the Exchange Act requires a showing of scienter, defined as “a …

Effective Strategies For Deterring Unethical Behavior In U.S. Financial Institutions, Samuel Akinlade Sanni

Walden Dissertations and Doctoral Studies

Unethical employee behavior in U.S. financial institutions continues to erode organizational trust and firms’ brand reputation. Leaders of financial institutions are concerned that unethical employee behavior can negatively impact firms’ profitability, competitiveness, and sustainability. Grounded in Kaptein’s corporate ethics virtue (CEV) model, the purpose of this qualitative pragmatic inquiry study was to explore how organizational ethical cultures influence employees’ behaviors. Data were collected through semistructured interviews with a sample of eight leaders in New York financial institutions, with a minimum of 5 years of successful experience in building a positive ethical environment, and by analyzing publicly available documents to support …

Stranger Disputes: When Artificial Intelligence Turns Arbitration Upside Down, Imre Stephen Szalai

Pepperdine Dispute Resolution Law Journal

Arbitration agreements are everywhere in the United States. These agreements already block access to courts in a troubling manner, and pursuant to these agreements, parties must resolve their disputes before a private, human arbitrator with broad, virtually unreviewable powers. However, with the growth of AI, companies could easily redraft their contracts to require arbitration before non-human bots or AI arbitrators instead of a human arbitrator. Based on the history, values, policy, and text of the Federal Arbitration Act (FAA), this Article concludes that the FAA would govern and support the use of an AI arbitrator. As a result, a pre-dispute …

Detecting Android Malware Based On Static Analysis Using Classification And Modified Clustering Techniques, Abdullah Allawi Al-Sraratee, Ahmed Habeeb Al-Azawei

Journal of Intelligent Informatics, Networking, and Cybersecurity

Because Android malware harms internet security, prior research proposes several different approaches to detect it accurately. However, such proposed models depend on numerous number of features to attain high accuracy. This could lead to high computation cost and potential overfitting. Furthermore, manual data labeling is labor-intensive, requiring significant human effort and skills. This research aims to: 1) extend previous literature on Android malware detection, 2) improve the accuracy of Android malware detection based on a low number of features, and 3) modify a clustering technique to group data into two different clusters to address the issue of unlabeled data. To …

Real-Time Fraud Detection Using Big Data, Abdulla Matar Alketbi

Theses

In today’s digital world, fraud detection has become an important part of financial security. This study explores and compares the performance of different machine learning models in identifying fraudulent transactions using the IEEE-CIS Fraud Detection dataset. Techniques such as Random Forest, Gradient Boosting, Deep Neural Networks, and Logistic Regression were evaluated. The dataset was pre-processed using SMOTE to balance the classes and improve model sensitivity to fraud cases. Each performance of the model was assessed using accuracy, precision, recall, and F1-score. The Random Forest model achieved the highest overall performance with an F1-score of 99.23

Usa V. Martins Inalegwu

2025 Decisions

USDC for the District of New Jersey

The Proactive Approach Of Artificial Intelligence In Monitoring And Analyzing Illegal Money Laundering Operations In The Virtual World, Ammar Yasser El-Bably

Journal of Police and Legal Sciences

The research dealt with the proactive method of artificial intelligence in monitoring and analyzing non-suspicious operations of money laundering in the virtual world, especially the Internet and dark web, where it dealt with the risks created for those networks and their exploitation for the benefit of terrorist and criminal organizations, money laundering, the purchase of weapons and drugs, human trafficking and financial fraud, where Internet users around the world reached 5 billion users, and the volume of money laundering crimes became 2.8 Trillion dollars, according to United Nations and International Monetary Fund statistics 2023 AI applications where algorithms, machine learning, …

Halo: Heterophily-Aware Label-Free Ordering For Unsupervised Graph Fraud Detection

Defensive Publications Series

Graph fraud detection (GFD) has rapidly advanced in protecting online services by identifying malicious fraudsters. Recent supervised GFD research highlights that heterophilic connections between fraudsters and users can greatly impact detection performance, since fraudsters tend to camouflage themselves by building more connections to benign users. Despite the promising performance of supervised GFD methods, the reliance on labels limits their applications to unsupervised scenarios; Additionally, accurately capturing complex and diverse heterophily patterns without labels poses a further challenge. To fill the gap, we propose a Heterophily-guided Unsupervised Graph fraud dEtection approach (HUGE) for unsupervised GFD, which contains two essential components: a …

Differential Privacy Enabled Deep Skin Image Classification Model Development, Prasun Kumar Mandal

Master’s Dissertations

Abstract In the era of big data, the explosive growth in data volume has significantly accelerated the development of deep learning. Deep learning is the most promising area of AI, yielding significant advancements in medical image classification. However, healthcare data contains important sensitive information and so privacy and security are crucial to preventing unauthorized access. Note that there are several data protection rules from multiple regulations to penalize any kind of data security violation, for example, the data protection principles (Article 5.1-2) and the data protection by design and by default (Article 25) of the General Data Protection Regulation from …

Reevaluating Fourth Amendment Protections In The Digital Age, Tiffany Benjamin

UC Law Constitutional Quarterly

No abstract provided.

Federated Learning Using Fully Homomorphic Encryption, Sk Golam Kuddus

Master’s Dissertations

Traditional machine learning approaches require centralizing data for training, which raises significant privacy concerns when dealing with sensitive information. Federated learning (FL) addresses this by keeping data local and enabling multiple users to collaboratively train a shared machine learning model. In spite of this, FL remains vulnerable to inference attacks, as sensitive information can still be extracted from the model’s learned parameters. While traditional privacy-enhancing techniques such as di!erential privacy introduce noise to model updates to obscure individual data points, they often present a fundamental trade-o! between privacy and utility. Furthermore, these approaches still carry risks of data leakage if …

The Settlement Privacy-Transparency Matrix: Moving Beyond The Dichotomous Mindset In The Settlement Debate, Alyson Carrel, Peter K. Chan

Journal of Dispute Resolution

The philosopher Arthur Schopenhauer once wrote, “The first forty years of life give us the text; the next thirty supply the commentary on it.” Forty years after Owen Fiss’s seminal article “Against Settlement,” it is time to reimagine how we approach the settlement debate beyond traditional dichotomies. Just as Schopenhauer recognized that time and experience allow us to better understand our past, forty years of grappling with the settlement debate have given us the perspective to see its limitations, and now with emerging technologies, new possibilities. The ‘text’ of the debate—its dichotomous framing and distributive solutions—has shaped four decades of …

How Investment Activity And Corporate Governance Affect The Disclosure Of Esg, Melisa Anggraini, Darsono Darsono, Danes Quirira Octavio

Jurnal Akuntansi dan Keuangan Indonesia

Background: This research investigates the impact of corporate governance elements and corporate investment activity on ESG disclosure in companies from Asia Pacific Emerging Markets. Methods: The study analyzes a sample of 150 companies from Asia Pacific Emerging Market countries over the period 2015–2022, yielding a total of 1200 observations. The Generalized Method of Moments-Difference (GMM-DIFF) is employed to test the hypothesis. Four independent variables related to corporate governance and one variable representing investment activity are used. Findings: The results show that investment in property, plant, and equipment assets, as well as the presence of audit committees, positively affect ESG disclosure …

Cyber Insurance For Public Housing: Confronting Market Barriers And Forging Policy Solutions, Deborah L. Johnson, William B. Simpson

Student Journal of Information Privacy Law

In 2023, the Los Angeles Public Housing Authority was hit by the LockBit ransomware gang, which claimed to have exfiltrated 15 terabytes of data. In another incident, hackers impersonated a vendor and diverted nearly $1 million in housing funds from a second California agency. Public Housing Authorities (PHAs), which handle large amounts of sensitive data, are increasingly being targeted by cyberattacks. These attacks often exploit the weak cyber defenses and broad risk profiles of these relatively unsophisticated entities. With limited resources and few avenues for recovery, PHAs are left vulnerable to, and by, cyberattacks, threatening the vital services they provide. …

The Case For A Federal Data Privacy Law From A National Security Perspective - What The U.S. Can Learn From Overseas, Theodore H. Massey Iii

Student Journal of Information Privacy Law

The collection of personal data in the private sector has grown exponentially over the years, leading to an exponential growth in the theft and the purchase of personal data by criminals and foreign adversaries. While the United States has implemented EO 14117 and the Protecting Americans’ Data from Foreign Adversaries Act of 2024 to protect against the inherent national security risks associated with data privacy, the United States must create an omnibus federal privacy law if it wishes to mitigate the national security risk. This paper introduces the reader to the increase in personal data collected by private organizations and, …

Strain In The System: Examining The Relationship Between Adverse Childhood Experiences And Pathways Into Cybercrime, Hannah R. Murphy

USF Tampa Graduate Theses and Dissertations

This study examines the relationship between adverse childhood experiences (ACEs) and cybercriminal behavior among 238 active hackers with verified website defacements identified through the Zone-H archive. Drawing upon General Strain Theory, this research investigates whether childhood trauma influences both the developmental timing and motivational aspects of hacking activities. Using ordinary least squares and ordered logistic regression analyses with country-clustered standard errors, the study tested two hypotheses: (1) that higher ACE scores would be associated with earlier onset of hacking behavior, and (2) that higher ACE scores would predict increased likelihood of engaging in revenge-motivated hacking. Results revealed a complex relationship …

The Positivity Effect In Contracts, Megan Kathryn Stricker

College of Science and Health Theses and Dissertations

The positivity effect is a noted preference for positive information over negative information in older adults’ cognition. This effect has been observed in multiple domains of cognition, including both attention and memory (Carstensen & Mikels, 2005). Evidence for the positivity effect has shown up consistently in replications across type of memory (Reed et al., 2014), and this phenomenon can be considered within the context of other trends of cognitive aging. Most if not all research in this area has been constrained to laboratory settings, and there has been limited research as to the context sensitivity of this observed effect. What …

Introduction: Forever Criminalized?: How Collateral Consequences Advance Disparities In Criminal Justice, Jenny Roberts

Hofstra Law Review

No abstract provided.

The Role Of Smart Actuarial Calculations In Improving The Quality Of Financial Reports Of The Iraqi Insurance Company, Zena Abdulstar Allayla, Waheed Mahmood Al-Ibrahimi

Journal of Economics and Administrative Sciences

The research aims to explore the role of actuarial calculations supported by artificial intelligence technologies (smart actuarial calculations) in enhancing the accuracy and quality of financial reporting. Companies face challenges related to the accuracy, transparency, and speed of preparing financial reports, necessitating innovative solutions based on smart technologies. The research analyzed the literature and employed an exploratory methodology to study the impact of smart actuarial calculations on the integrity of financial reports. The association between the application of artificial intelligence and professional standards and their impact on the performance of insurance companies and financial reporting practices was also examined. The …

Community Detection In Heterogeneous Information Networks Without Materialization, Jiaxin Jiang, Siyuan Yao, Yuhang Chen, Bingsheng He, Yudong Niu, Yuchen Li, Shixuan Sun, Yongchao Liu

Research Collection School Of Computing and Information Systems

Community detection in heterogeneous information networks (HINs) poses significant challenges due to the diversity of entity types and the complexity of their interrelations. While traditional algorithms may perform adequately in some scenarios, many struggle with the high memory usage and computational demands of large-scale HINs. To address these challenges, we introduce a novel framework, SCAR, which efficiently uncovers community structures in HINs without requiring network materialization. SCAR leverages insights from meta-paths to interpret multi-relational data through compact vertex-based sketches, significantly reducing computational overhead and materialization overhead. We propose a sketch-based technique for estimating changes in modularity, improving both the precision …