  The effectiveness and efficiency of 5G software stack vulnerability and
unintended behavior detection are essential for 5G assurance, especially for
its applications in critical infrastructures. Scalability and automation are
the main challenges in testing approaches and cybersecurity research. In this
paper, we propose an innovative approach for automatically detecting
vulnerabilities, unintended emergent behaviors, and performance degradation in
5G stacks via run-time profiling documents corresponding to fuzz testing in
code repositories. Piloting on srsRAN, we map the run-time profiling via
Logging Information (LogInfo) generated by fuzzing test to a high dimensional
metric space first and then construct feature spaces based on their timestamp
information. Lastly, we further leverage machine learning-based classification
algorithms, including Logistic Regression, K-Nearest Neighbors, and Random
Forest to categorize the impacts on performance and security attributes. The
performance of the proposed approach has high accuracy, ranging from $ 93.4 \%
$ to $ 95.9 \% $, in detecting the fuzzing impacts. In addition, the proof of
concept could identify and prioritize real-time vulnerabilities on 5G
infrastructures and critical applications in various verticals.

Cryptography and Security;Machine Learning;Software Engineering