Math and Computer Science
NNU computer science majors are required to complete a senior research project. This requirement has been highly praised by employers of NNU graduates.
Graduate John Donaldson who attended the Naval Postgraduate School and now works for Lawrence Livermore National Laboratory said, “When I’ve talked to my program managers [at the Naval Postgraduate School] who have worked with NNU students in the past, they like the fact that in our computer science program we are required to produce a senior project and thesis which a lot of other schools don’t require.”
Math and computer science faculty are researching as well as maintaining and improving the department’s labs and equipment.
2021 Student Research Highlight
Title: Identifying ATT&CK® Tactics in Android Malware Control Flow Graph Through Graph Representation Learning and Interpretability.
Thesis Adviser: Dr. Dale Hamilton, Dr. Kevin McCarty
Malware affects millions of machines, causing havoc to those it reaches. The dangers and negative impact that malware has inflicted push researchers to find a way to mitigate its effects. Labeling malware within the anti-malware services becomes a challenge in finding the correct Tactics, Techniques, and Procedures (TTP) that each malware implements. The Control Flow Graph (CFG) describes the structure of a program during its execution; this is how a program flows. In reference to malware, it represents the flow of all the internal and external function calls. The current research proposes a novel approach to locating ATT&CK® TTP in a CFG by applying Machine Learning Classifiers on Android Malware. Through these methods, the approach associates the TTP, given by the ATT&CK® Framework with a subgraph of an Android malware CFG. Using Graph Neural Network and SIR-GN node representation learning approach, this methodology processes the CFG and creates a model that classifies the associated TTP. Furthermore, the explanation technique SHapley Additive exPlanations (SHAP), a model agnostic game-theoretic approach to explain any machine learning model’s output and identify the subgraph in the CFG connected with the specific TTP, is implemented. Preliminary experiments indicate approximately 89% accuracy in classifying such techniques.
Jeffrey’s research has been published three times, making him a 3-time peer-reviewed published author. Jeffrey also presented his research at the SigmaXi, AAAI, and IEEE BigData Conferences. Jeff plans to attend a full-ride scholarship to Penn State to pursue his Ph.D.
Title: Styling Updates to the Application Status Page for Northwest Nazarene University.
Thesis Adviser: Dr. Dale Hamilton
The Application Status Portal is a tool within Slate, the Customer Relationship Management (CRM) system used by the admissions teams of Northwest Nazarene University (NNU) and several other universities, to assist students in the application process by providing information about needed, completed, and or missing documentation and forms. Slate was introduced three years ago at NNU and has seen work from the university’s CRM manager, Sage Mwiinga, in the setup and configuration for the use of many individuals from the university. The application status portal has not seen many updates in the realm of styling since it was first implemented in the summer of 2018. Because of that, elements of the portal do not contain a consistent styling or culture across the portal. The purpose of this project was to utilize CSS classes to add design elements to make the portal stand out while also being similar in concept to the main NNU website for users on various device types. The resulting portal cultivated a familiar feel to the nnu.edu domain using positioning and brand colors that shows relation and connection throughout the portal and to the NNU website. Future work on the portal will see the need of implementing Tailwind CSS, a CSS framework that is better suited for making responsive websites.
Title: Detecting Stock Market Patterns via Standard Query Language Data Analytics.
Thesis Adviser: Dr. Kevin McCarty
Predictive analysis within the stock market has been a goal of many different banks and large organizations as well as individual traders, as there are substantial monetary gains to be had. The objective of this project is to ask the question: Can one use patterns developed within the stock market to predict behavior and achieve positive financial margins? When beginning this research endeavor, learning the current patterns for trading algorithms is necessary. Traders often use technical analysis to predict future stock moves. With this information and a sample database of around nineteen years of stock data, this hypothesis was tested on the Golden and Death Cross. Using specialized SQL queries these patterns were investigated through a series of tables and extensively explored to provide relevant data needed to achieve a prediction method for the trading algorithm. The results demonstrated that the opposite hypothesis, buying on the Death Cross and selling on the Golden Cross, occurred when employing these patterns implemented by this approach.