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Education As A Solution To Combat Rising Cybercrime Rates Against Children And Teenagers, Christian Javier Solis-Diaz
Education As A Solution To Combat Rising Cybercrime Rates Against Children And Teenagers, Christian Javier Solis-Diaz
Electronic Theses, Projects, and Dissertations
Ninety seven percent (97%) of people between the ages of 3 and 18 are found to be users of technology and internet services daily. This number also correlates with rising cyber crime rates against people in this age bracket. It is found that people between 3 and 18 years old are found to be technologically savvy but often lack the knowledge of how to protect themselves in online environments. Researchers have suggested that cybersecurity awareness training is an effective method at combating common forms of cyberattack such as social engineering. Social engineering attacks are found to make up 98% of …
Improving Credit Card Fraud Detection Using Transfer Learning And Data Resampling Techniques, Charmaine Eunice Mena Vinarta
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 …
Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh
Early-Warning Prediction For Machine Failures In Automated Industries Using Advanced Machine Learning Techniques, Satnam Singh
Electronic Theses, Projects, and Dissertations
This Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier …
A Systematic Literature Review Of Ransomware Attacks In Healthcare, Jasler Klien Adlaon
A Systematic Literature Review Of Ransomware Attacks In Healthcare, Jasler Klien Adlaon
Electronic Theses, Projects, and Dissertations
This culminating experience project conducted a Systematic Literature Review of ransomware in the healthcare industry. Due to COVID-19, there has been an increase in ransomware attacks that took healthcare by surprise. Although ransomware is a common attack, the current healthcare infrastructure and security mechanisms could not suppress these attacks. This project identifies peer-viewed literature to answer these research questions: “What current ransomware attacks are used in healthcare systems? “What ransomware attacks are likely to appear in the future?” and “What solutions or methods have been used to prepare, prevent, and recover from these attacks?” The purpose of this research is …
Healthcare Data Breaches: Analysis And Prevention, Nikita S. Dean
Healthcare Data Breaches: Analysis And Prevention, Nikita S. Dean
Electronic Theses, Projects, and Dissertations
It is evident that the healthcare sector continues to experience data breaches. This culminating experience project focuses on the need to maintain patient data privacy, minimize financial risks, and address public health concerns. The study examined data collected from U.S. Department of Health and Human Services from 2018 to 2023 to answer the following research questions: Q1. How many individuals are affected due to data breaches in healthcare & which States had the most affected individuals? Q2. What are the most common causes of healthcare data breaches & what measures can be taken to prevent this? and Q4. What are …