Digital Commons Network^™

The Impact Of The 2008 Financial Crisis On Crime And Cybercrime In The United States, Rashed Adel Alsuwaidi

Theses

This thesis will examine how the traditional crime and cy- were affected by the 2008 financial crisis. United States are also experiencing a rise in crime between 2005 and 2012. Based on the FBI data at the national level. The Internet Crime Complaint Center (IC3), Uniform Crime Reports and important economic indicators. The study, which involves tors, including unemployment, GDP, rates of foreclosures, and mortgage rates, is a combination. correlationbased interpretation supported by exploratory descriptive trend analysis. model-fit checks. The results indicate that contrary to the conventional expectations, violent and property crime also maintained their long-term reduction during the period …

Detecting Fraud In Police Reports Using Machine Learning And Natural Language Processing, Maryam Almarar

Theses

The paper explores how statistical analysis and machine learning can be used to identify the fraud patterns in the police reports. The study aims at establishing the most important predictive factors and indicators distinguishing fraudulent and valid cases with the use of structured data of police databases. The work was done in the background of the increase in financial fraud instances and the rising necessity of the introduction of automated detection systems in police departments. Police reports of the pastwere mined down to data and analyzed on SPSS 1, to carry out statistical operations. The sample was structured data which …

Rise Of Social Media Hacking: Ai-Based Ip Tracking For Uae Law Enforcement, Mounikha Naarrayen Chakravarthula

Theses

Social media has evolved into a critical channel for communication, expression, and public influence, but it has also become a prevalent avenue for cybercrime, particularly in digitally advanced nations such as the United Arab Emirates (UAE). The rising complexity of online offences, coupled with anonymisation tools and cross-border digital behaviour, has made the attribution of social-media-based cyber incidents increasingly challenging for law enforcement. In this context, artificial intelligence (AI) offers the potential to strengthen digital investigations by providing intelligent, scalable, and evidence-driven attribution capabilities. This research develops an AI-assisted Internet Protocol (IP) attribution framework tailored specifically for UAE law enforcement …

Deepfake Audio Detection, Rashed Alfalasi

Theses

The rise of deepfake audio technology has introduced a serious threat to information credibility, personal security, and media integrity. This thesis investigates the application of machine learning techniques for detecting synthetic audio through the analysis of acoustic features, including Mel-Frequency Cepstral Coefficients (MFCCs), spectral centroid, chroma_stft, and zero-crossing rate. The dataset used in this study was sourced from Kaggle and contains labeled samples of real and fake audio clips. The research aimed to train and evaluate multiple machine learning models—Support Vector Machines (SVM), Random Forest, XGBoost, Logistic Regression, and Neural Networks—to determine the most effective approach for deepfake audio classification. …

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

Criminal Activity Monitoring System: Targeting Africans And East Asian Communities In Dubai, Hamdan Kalantar

Theses

This dissertation investigates crime patterns among African and East Asian communities in Dubai, focusing on geographic hotspots, temporal trends, victim demographics, and the use of machine learning for predictive crime modeling. The research addresses the challenges faced by these communities, including labor exploitation, financial fraud, and human trafficking, within the socio-economic context of a rapidly growing urban city. Building on existing studies of urban crime, this research aimed to explore the spatial, temporal, and demographic dynamics of crimes and assess the potential of predictive models to assist law enforcement in crime prevention and resource allocation. A mixed-methods approach was employed, …

Deepfake Image Detection Using Explainable Ai And Deep Learning, Abdallah Abdulfattah Mohammed Abdulrahman

Theses

Deepfake technology has grown exponentially, and it has changed our perspective of managing digital content. The technology has many applications, but its ease of access has brought many risks. These risks include identity theft, misinformation spread, development of non-consensual content, and a decrease in trust in media. To address these challenges, we propose a hybrid deep learning model combining Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for deepfake image detection. We developed a CNN model and four hybrid CNN-LSTM models in our study. Our approach integrates the strengths of CNNs for spatial feature extraction and LSTMs for …

Ai-Driven Anomaly Detection In Cybersecurity, Mohamed Almansoori

Theses

India has witnessed a huge level of digitization in the last two decades which has greatly impacted its economy and society. But the increasing use of technology has also intensified the number and level of threats to cyberspace in the country. Signature-based detection systems that have dominated the cybersecurity field for years have not been up to the challenge of modern attackers who use more complex and diverse attack methods. These challenges are well understood and this research focuses on improving the cybersecurity anomaly detection in India during the period between 2003 and 2022 using advanced AI and machine learning. …

Smart Credit Card Fraud Detection Using Machine Learning, Aisha Bin Sulaiman, Rowdha Masood Falaknaz

Theses

This thesis addresses the increasing issue of fraud resulting from technological advancements affecting both customers and fraudsters, while law enforcement agencies face challenges of inadequate case prioritization; data overload and slow investigative analysis. The central objective of this thesis revolves around strategies to counter these challenges, emphasizing the urgency of rapid response to major alert cases that can lead to widespread impacts. The authors' experience in financial fraud detection and credit card crime investigation within the police department has inspired this research, guiding the approach toward practical solutions for everyday operational challenges. The significance of this thesis lies in its …

Assessing The Effectiveness Of Law Enforcement Strategies In Combating E-Crime Using Ai, Mohammad Ahmad Alkhazraji

Theses

The rise of e-crime, particularly international online scams, has presented significant challenges to law enforcement agencies around the world. Scams have become transnational, exploiting the internet's interconnectivity to deceive victims from different countries. Law enforcement agencies have a severe uphill battle in trying to combat these types of crimes because of technological and jurisdictional limitations and resource accessibility. This research has analyzed the effectiveness of current policing against international online scams, focusing on the challenges encountered thus far and possible solutions. The critical research objectives revolve around assessing law enforcement agencies' present strategies, identifying the main barriers that these agencies …

Analyzing Crime Patterns Through Artificial Intelligence, Rashid Khalid Mohamed Abdalla Alhammadi

Theses

This paper examines how crime patterns affect predictive policing in Denver, Colorado. Modern crimes like cybercrime and identity theft have challenged traditional crime research methods, necessitating data-driven ones. In this context, the study examines how machine learning and AI can identify and forecast crime trends across neighborhoods, timeframes, and other aspects. The research sought to understand crime's dynamics and socioeconomic effects. The study collected data using a mixed-method approach based on questions about crime kinds, rates, and sociodemographic characteristics. A large chunk of the data came from public databases and targeted neighborhood surveys. Regression analysis, decision trees, and neural network …

A Domain-Specific Language For Accounting, Janhavi Doshi

Theses

Accounting can be loosely described as a set of rules and best practices for recording and reporting financial transactions. Despite the seemingly straightforward nature of these principles, implementing them through programming has proven to be complex. Currently, over 550 accounting software solutions exist, developed using more than 17 programming languages. These diverse and often fragmented solutions have led to numerous instances of fraud and errors that elude early detection due to inadequate validity checks. This study investigates the need for a unified approach by developing a Domain-Specific Language (DSL) for Accounting. The proposed DSL aims to standardize accounting practices, enhance …

An Integrated Framework For Video-Based Deepfake Forensic Analysis, Bashaer Mohamed Alsalami

Theses

Deepfakes are AI-generated synthetic media that use AI algorithms to mimic human faces, voices, and actions with high levels of realism. They utilize advanced machine learning and deep learning techniques to generate realistic synthetic media, making the distinction between real and fake content increasingly difficult. Deepfakes were initially developed for entertainment and creative applications. However, they are currently being misused in various domains, posing significant security and ethical challenges. These risks necessitate effective deepfake detection systems to mitigate potential harm and preserve the integrity of digital communication. Nonetheless, the existing detection methods are inadequate and lack a holistic detection mechanism, …

Predicting Cost Contingency In Retrofit Projects Using Machine Learning Techniques, Dana Amin

Theses

Project contingency is one of the most important preliminary processes in any project estimation as it ensures the project risk for cost over-run due to uncertainties , in light of that, it is also important to make sure that the project contingency is not overestimated to maintain competitiveness in the market. This project aims to develop contingency decision support tool using ML techniques in order to predict the optimum contingency cost that balances between maintaining business competitiveness in the market and achieving project objectives. Different ML ways were evaluated and based on accuracy level ; Random Forest was found to …

Application Of Ai In Financial Sector: Earnings Call Dataset Analysis, Kesa M. Abbas

Theses

Deep learning has emerged as a cornerstone in diverse scientific domains, demonstrating profound implications both in academic research and conventional applications. The utilization of deep learning has branched to the financial sector for stock prediction, management of assets, credit score analysis, etc. During the end of the financial fiscal year, top executives present their companies’ growth and losses in earnings calls. These earnings calls invariably webcasted, furnish stakeholders with insights into a firm’s fiscal performance during a given period. These audios along with transcripts are published on the company website for the general public. If these audio and transcripts are …

Graph Neural Networks For Ethereum Fraud Detection, Charity Mwanza

Theses

Detecting fraudulent transactions on the Ethereum network can help cryptocurrency companies that operate on the Ethereum platform protect their users from exposure to fraudsters. The most common fraudulent activities in the cryptocurrency network include phishing and smart Ponzi schemes. Since cryptocurrency technology is still young, most investors lack knowledge of how the smart contacts used in the Ethereum platform operate; hence, they cannot evaluate the risks they are exposed to when carrying out cryptocurrency transactions. The key role that this paper looks at is the application of graph neural networks in the extraction of features of users in the Ethereum …

User Behavior Analysis For User Profile Prediction, Islam Al Qaisi

Theses

The importance of user behavior analysis is becoming increasingly valuable to Software as a Service (SaaS) businesses such as EDI (Energy Data Intelligence). Users of big data applications require attention in order not to lose customer loyalty in a competitive business environment. With data analytics techniques, knowledge can be extracted about the user behaviors, and that would be a beneficial exercise to the business when it comes to improving services to the customers whether it’s a tailored customer support, targeted marketing campaigns or enhanced product features. This capstone project is focused on predicting user profiles through their web usage behavior. …

Fraud Detection In Financial Services Using Machine Learning, Khalifa Alsenaani

Theses

The banking industry is an important part of modern actions as it manages the movement of funds between different parties. However, this area is synonymous with some cases of fraud where people are being swindled their money, illegal transactions are being made and others.

The complexity of ensuring that transactions stay legitimate has since made it almost impossible to regulate fraud in this industry correctly. This report presents an approach that utilizes Machine Learning techniques to build a model that detects fraudulent transactions and flags them. The approach utilizes a dataset that contains a collection of observation points on transactions …

Fraudulent Insurance Claims Detection Using Machine Learning, Arif Ismail Alrais

Theses

As the different countries around the world evolve into a more economical-based and stimulating their economy is the goal. The main purpose of most of these countries is to fight off money launderers and fraudsters for better economic growth. A popular fraud topic in this regard is insurance fraud since it costs the companies and the public billions. Applying data analysis and machine learning are great ways used to address many problems regarding any automated system. To address this problem, first extensive research should be made to check out what has been applied and what the most promising solution using …

Credit Card Fraud Detection Using Machine Learning, Meera Alemad

Theses

The purpose of this project is to detect the fraudulent transactions made by credit cards by the use of machine learning techniques, to stop fraudsters from the unauthorized usage of customers’ accounts. The increase of credit card fraud is growing rapidly worldwide, which is the reason actions should be taken to stop fraudsters. Putting a limit for those actions would have a positive impact on the customers as their money would be recovered and retrieved back into their accounts and they won’t be charged for items or services that were not purchased by them which is the main goal of …

Predictive Policing, Amna Ali Al Boom, Shaikha Khalid Bin Thani

Theses

UAE is one of the safest countries to live in, but that does not indicate that the country does not witness crimes, During the COVID-19 pandemic, the country saw an increase in cyber and digital crimes. Apart from cybercrime, there are other types of crimes, such as street crimes and violent crimes. Data analytics aids Dubai Police to predict crimes. Criminal investigation is one of the fields that is very interesting and is taught in colleges and academies. Data analytics opens the door for studying the details of each crime. Data mining tools consist of a variety of techniques that …

Fraud Detection Using Data Analytics, Ayesha Karmustaji

Theses

At present, the biggest concern of every organization is to detect and control financial fraud. Tax frauds cause the loss of billions of dollars every year. As a result, data mining techniques are used to combat the growing problem of tax fraud. Tax evasions cause a reduction in revenue collection. It also has a bleak impact on government policies and budget. The goal of this study is to describe the use of data analytics tools to process and analyze tax data related to value-added tax evasions. This study is a conceptual perspective that provides a theoretical and methodological basis for …

Financial Fraud Detection Using Machine Learning Techniques, Matar Al Marri, Ahmad Alali

Theses

Payments related fraud is a key aspect of cyber-crime agencies and recent research has shown that machine learning techniques can be applied successfully to detect fraudulent transactions in large amounts of payments data. Such techniques have the ability to detect fraudulent transactions that human auditors may not be able to catch and also do this on a real time basis. In this project, we apply multiple supervised machine learning techniques to the problem of fraud detection using a publicly available simulated payment transactions data. We aim to demonstrate how supervised ML techniques can be used to classify data with high …

Risk Processing, Affect, And Efficacy In Online Privacy Behavior, Michael Shreeves

Theses

Cybersecurity research has indicated that people use simple heuristics to assess online privacy risk. Online warnings typically operate by encouraging systematic risk processing. A review of the literature on risk perception suggested that negative emotional affect produced by risk descriptions can both increase systematic processing and independently increase perceived risk. A review of fear appeals in health behavior research suggested that risk communications that produce fear will be rejected if an effective behavioral response recommendation is not provided. This study investigated the (a) impact of emotional vividness in warning descriptions and (b) response information presence on disclosure of a single …

The Correlation Between Financial Fraud And Economic Downturn, Karen Alyse Hyink

Theses

Throughout the 20th century, researchers studied financial fraud in order to better understand the factors, motives and environments that increase the occurrences of fraud. By studying these historical trends, modern governmental agencies and public organizations are able to more effectively detect and prevent financial fraud.

Economic climate directly influences the occurrence of fraud; specifically, it is during periods of economic downturn when organizations are more vulnerable to fraudulent attacks. While it has always been assumed that recessionary periods lead to an increase in fraudulent activities, the direct correlation between the two has been greatly understudied by researchers. This thesis is …

Recommendations For A Comprehensive Identity Theft Victimization Survey Framework And Information Technology Prevention Strategies, Sara Berg

Theses

While steps have been undertaken in the last five years to better understand the problem of identity theft, there has been little research done in the areas of high tech crime victim profiling and prevention. Most studies focus on victim demographics without examining ways in which the victimization may have been facilitated technologically. Attempts to look at precipitating behaviors in the context of victimization are limited. As such, a weak empirical base exists on which to generate additional research and potential solutions to identity theft victimization. This thesis bridges previous identity theft research with other empirical studies in order to …