Nov, 2023
Conference Paper
Shifting Left for Machine Learning: An Empirical Study of Security Weaknesses in Supervised Learning-based Projects
Context: Supervised learning-based projects (SLPs), i.e., software projects that use supervised learning algorithms, such as decision trees are useful for performing classification-related tasks. Yet, security weaknesses, such as the use of hard-coded passwords in SLPs, can make SLPs susceptible to security attacks. A characterization of security weaknesses in SLPs can hel…