  Connected autonomous vehicles (CAVs) are anticipated to have built-in AI
systems for defending against cyberattacks. Machine learning (ML) models form
the basis of many such AI systems. These models are notorious for acting like
black boxes, transforming inputs into solutions with great accuracy, but no
explanations support their decisions. Explanations are needed to communicate
model performance, make decisions transparent, and establish trust in the
models with stakeholders. Explanations can also indicate when humans must take
control, for instance, when the ML model makes low confidence decisions or
offers multiple or ambiguous alternatives. Explanations also provide evidence
for post-incident forensic analysis. Research on explainable ML to security
problems is limited, and more so concerning CAVs. This paper surfaces a
critical yet under-researched sensor data \textit{uncertainty} problem for
training ML attack detection models, especially in highly mobile and
risk-averse platforms such as autonomous vehicles. We present a model that
explains \textit{certainty} and \textit{uncertainty} in sensor input -- a
missing characteristic in data collection. We hypothesize that model
explanation is inaccurate for a given system without explainable input data
quality. We estimate \textit{uncertainty} and mass functions for features in
radar sensor data and incorporate them into the training model through
experimental evaluation. The mass function allows the classifier to categorize
all spoofed inputs accurately with an incorrect class label.
