Phishing attacks are a major cybersecurity threat because attackers often use fake or misleading emails to make their messages appear legitimate and gain users trust. These emails can trick users into sharing sensitive information such as passwords, financial data, and personal information. As phishing emails become more advanced, they are also becoming more difficult for individuals and organizations to recognize. This study focuses on whether machine learning can identify phishing emails more effectively than humans. To test this hypothesis, a Random Forest model was built using a dataset of 101,120 phishing and legitimate emails. The email text was converted into numerical features using Term Frequency Inverse Document Frequency TFIDF so the model could process the information. After the model was trained and tested using labeled email data, the Random Forest model performed better than the Logistic Regression model and reached an accuracy of 98.64 percent.

By researching if existing parametric schedulers priority functions like HEFT and CPOP returns a task order that results in the lowest makespan is essential to understand why HEFT and CPOP use their specific priority functions as well as give us ideas to improve these schedulers priority functions for future use. We hypothesize that HEFT and CPOP will both produce a task sort that results in a higher or equal makespan compared to the alternative priority function we used on these schedulers. All work was done on an open-source library for comparing task scheduling algorithms called SAGA.