  Adversarial example detection plays a vital role in adaptive cyber defense,
especially in the face of rapidly evolving attacks. In adaptive cyber defense,
the nature and characteristics of attacks continuously change, making it
crucial to have robust mechanisms in place to detect and counter these threats
effectively. By incorporating adversarial example detection techniques,
adaptive cyber defense systems can enhance their ability to identify and
mitigate attacks that attempt to exploit vulnerabilities in machine learning
models or other systems. Adversarial examples are inputs that are crafted by
applying intentional perturbations to natural inputs that result in incorrect
classification. In this paper, we propose a novel approach that leverages the
power of BERT (Bidirectional Encoder Representations from Transformers) and
introduces the concept of Space Exploration Features. We utilize the feature
vectors obtained from the BERT model's output to capture a new representation
of feature space to improve the density estimation method.
