YS-2 Generative AI & Cybersecurity

Team Members

Overview

The purpose of this website is to host online training modules for the impacts of Generative Artificial Intelligence (Gen AI) on Cybersecurity. The goal of these modules is to help Computer Science and/or IT related research-oriented users or other interested individuals in understanding the improvements and threats that Generative AI effects on the security of a software system.

With the recent rise of AI and other Machine Learning components, there are an overwhelmingly number of new solutions that simplify tasks and gain efficiency in various fields. For example, artificial intelligence uses data analysis and decision-making in important and interesting fields like computer vision, malware detection, and drug discovery. 

Generative AI, in particular, involves learning with existing data and generating future data with various machine learning models. When comparing a Cybersecurity of a system with this, you may find numerous ways in which they intersect. Gen AI could either positively or negatively impact a software's security. Despite the great importance of this topic in the current workforce and society, the research upon this it extremely limited.

This project was conducted to educate future students, educators, and workforce with the development of five learning modules. Each is comprised of a pre-lab, hands-on lab, and a post-lab. The pre-lab mainly conceptualizes the main components of the current module and its learning objectives. The hands-on lab gives an opportunity for readers to conduct simulations on their own. The post-lab is additional to the module for individual enhancement and knowledge in a form of a homework. In addition, supplemental materials such as pictures or videos would be provided along with the instructive text. 

The modules covered in this website are the Anomaly Detection Module, Intrusion Detection System (IDS) Module, Malware Analysis Module, Deepfake Phishing Simulation Module, and the Polymorphic Malware Simulation Module. Some modules are considered as an influence towards Cybersecurity, while some are threats.

Development of the example codes in this site were done through the open-source Google CoLaboratory (Google CoLab) environment. It is a Jupyter Notebook service that requires no setup to use and provides free access to computing resources such as GPUs. It helps with this project since it allows students to access and flexibly practice the modules. 

The project website would likely include more case studies, extensive labs, and conclusions. It may be also used to further develop algorithms in which Generative AI does not threaten Cybersecurity and/or improves it. This project website will help enhance the Gen AI and Cybersecurity curricula across computing disciplines, engage student active learning, and increase problem-solving capability.

Modules:

0: Getting Started 

1: Anomaly Detection Module 

2: IDS Module 

3: Malware Analysis Module 

4: Deepfake Phishing Simulation Module 

5: Polymorphic Malware Simulation Module