Case Study: Lockheed Martin Space Systems achieves automated 5G vulnerability analysis with MathWorks Reinforcement Learning Toolbox

Lockheed Martin Assesses 5G Network Vulnerabilities with Reinforcement Learning Toolbox

Lockheed Martin Space Systems needed a way to uncover and assess emerging 5G attack vectors across a complex, dynamic network stack where traditional methods fell short. Working with MathWorks, the team used MATLAB Reinforcement Learning Toolbox alongside a synthetic EXata digital-twin environment to model 5G components, user/host behaviors, and adversarial tactics to expose vulnerabilities early in the design process.

MathWorks' MATLAB Reinforcement Learning Toolbox trained adversarial agents in closed-loop with EXata to optimize attack patterns and evaluate mitigations; the experiment ran 3,000 episodes (8,246 agent steps), achieved the maximum reported reward (5/5) and a claimed 100% accuracy for the validation metrics, and completed training in roughly 3.5 hours. The MathWorks solution sped development with built-in math libraries and a GUI, enabling Lockheed Martin Space Systems to identify threat vectors, test mitigation techniques, and improve 5G security assessment fidelity.

Lockheed Martin Space Systems

Ambrose Kam

Lockheed Martin