This month we bring you this user story from Lockheed Martin, which really struck a chord with the team here at CES and addresses the topic of network vulnerabilities.
Lockheed Martin Rotary and Mission Systems (RMS) employs over 35 thousand people globally and develops a broad range of products and technologies. Recently, the cybersecurity team at RMS started working on assessing potential network vulnerabilities in the company’s 5G infrastructure. This included an assessment of data security, user privacy, confidentiality, integrity, and availability. A traditional audit style approach was never going to suit these complex problems.
To assess gaps, the team used MATLAB® and Reinforcement Learning Toolbox™ to come up with attack scenarios. Work began with the building of 5G models using EXata Cyber emulation software and the definition of a set of threat vectors based on security frameworks from the 3rd Generation Partnership Project (3GPP), National Security Agency (NSA), and other organisations. The team then used Reinforcement Learning Toolbox to train an adversarial deep Q-network agent that would optimise attack patterns and find worst-case scenarios. Based on these results, the team identified mitigation techniques to address the discovered vulnerabilities.
The team at RMS is now exploring opportunities to increase the fidelity of their simulations using 5G Toolbox™ and achieve better realism with a multi-agent reinforcement learning framework enabled by Simulink®.
More on this story here