A Personalized Learning Framework for Software Vulnerability Detection and Education

The software has become a necessity for many different societal industries including, technology, health care, public safety, education, energy, and transportation. Therefore, training our future software developers to write secure source code is in high demand. With the advent of data-driven techniques, there is now a growing interest in leveraging machine learning and natural language processing (NLP) as a source code assurance method to build trustworthy systems. In this work, we propose a framework including learning modules and hands-on labs to guide future IT professionals towards developing secure programming habits and mitigating source code vulnerabilities at the early stages of the software development lifecycle following the concept of Secure Software Development Life Cycle (SSDLC). In this research, our goal is to prepare a set of hands-on labs that will introduce students to secure programming habits using source code and log file analysis tools to predict, identify, and mitigate vulnerabilities. In summary, we develop a framework which will (1) improve students' skills and awareness on source code vulnerabilities, detection tools and mitigation techniques (2) integrate concepts of source code vulnerabilities from Function, API and library level to bad programming habits and practices, (3) leverage deep learning, NLP and static analysis tools for log file analysis to introduce the root cause of source code vulnerabilities.