Document Type
Event
Start Date
23-4-2023 5:00 PM
Description
Financial fraud has become an increasingly prevalent and sophisticated issue in the modern world, causing significant losses for individuals, businesses, and economies alike. The exponential growth of digital transactions has only accelerated the need for effective and efficient fraud detection systems to safeguard these financial ecosystems. Traditional machine learning techniques, such as Neural Networks (NNs), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), have played a crucial role in mitigating the impact of fraud by employing advanced algorithms to identify and prevent fraudulent transactions. With the recent advent of quantum computing and its potential to revolutionize the field of machine learning, there has been growing interest in exploring quantum algorithms for financial fraud detection. Quantum machine learning models are believed to offer significant computational advantages over their classical counterparts, which may lead to improved fraud detection capabilities. This Project aims to provide a comprehensive comparison of classical machine learning models, specifically NNs, KNN, and SVM, with their quantum counterparts in the context of credit card fraud detection. We employ the widely-used Kaggle Credit Card Fraud Dataset, which contains a diverse set of anonymized transactional data labeled as genuine or fraudulent. Our comparative analysis focuses on assessing the performance of each model in terms of accuracy, precision, recall, F1-score, and computational efficiency. In addition, we also introduce the BPF-extended tracking honeypot (BETH) dataset as the first cybersecurity dataset for uncertainty and robustness benchmarking. We examine the potential application of quantum machine learning models for IoT device security using the BETH dataset, which contains modern host activity and attacks collected from cloud services. Our results highlight the potential benefits of quantum machine learning models for financial fraud detection and IoT device security.
UC-388 Quantum Computing in Cybersecurity Related Data Analysis : A Comparative Analysis of Classical and Quantum Machine Learning Models on Various Security Threats
Financial fraud has become an increasingly prevalent and sophisticated issue in the modern world, causing significant losses for individuals, businesses, and economies alike. The exponential growth of digital transactions has only accelerated the need for effective and efficient fraud detection systems to safeguard these financial ecosystems. Traditional machine learning techniques, such as Neural Networks (NNs), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), have played a crucial role in mitigating the impact of fraud by employing advanced algorithms to identify and prevent fraudulent transactions. With the recent advent of quantum computing and its potential to revolutionize the field of machine learning, there has been growing interest in exploring quantum algorithms for financial fraud detection. Quantum machine learning models are believed to offer significant computational advantages over their classical counterparts, which may lead to improved fraud detection capabilities. This Project aims to provide a comprehensive comparison of classical machine learning models, specifically NNs, KNN, and SVM, with their quantum counterparts in the context of credit card fraud detection. We employ the widely-used Kaggle Credit Card Fraud Dataset, which contains a diverse set of anonymized transactional data labeled as genuine or fraudulent. Our comparative analysis focuses on assessing the performance of each model in terms of accuracy, precision, recall, F1-score, and computational efficiency. In addition, we also introduce the BPF-extended tracking honeypot (BETH) dataset as the first cybersecurity dataset for uncertainty and robustness benchmarking. We examine the potential application of quantum machine learning models for IoT device security using the BETH dataset, which contains modern host activity and attacks collected from cloud services. Our results highlight the potential benefits of quantum machine learning models for financial fraud detection and IoT device security.