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With the widespread prevalence of modern vehicles that entrust control to onboard computers, new research looks toward a larger US Army effort to invest in greater cybersecurity protection measures for its aerial and land platforms, especially heavy vehicles.
US Army researchers developed a new machine learning-based framework to enhance the security of computer networks inside vehicles without undermining performance.
The new technique called DESOLATOR is expected to help optimize a well-known cybersecurity strategy known as the moving target defense.
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The technology was devised by an international team of experts from Virginia Tech, the University of Queensland, and Gwangju Institute of Science and Technology, researchers at the U.S. Army Combat Capabilities Development Command, known as DEVCOM, and Army Research Laboratory.
DESOLATOR, which stands for deep reinforcement learning-based resource allocation and moving target defense deployment framework, helps the in-vehicle network identify the optimal IP shuffling frequency and bandwidth allocation to deliver effective, long-term moving target defense.
The research team used deep reinforcement learning to gradually shape the behavior of the algorithm based on various reward functions to ensure that DESOLATOR took both security and efficiency into equal consideration.
The technique is not limited to identifying the optimal IP shuffling frequency and bandwidth allocation. Since this approach exists as a machine learning-based framework, other researchers can modify the technique to pursue different goals within the problem space, according to armyrecognition.com.
The research paper was published in the peer-reviewed journal IEEE Access.
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