NLP-based Cross-Layer 5G Vulnerabilities Detection via Fuzzing Generated Run-Time Profiling. (arXiv:2305.08226v1 [cs.CR])

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 …

applications assurance automation behavior detection challenges critical cybersecurity detection efficiency fuzzing generated main nlp profiling research run scalability software testing vulnerabilities vulnerability