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In-vehicle networking protocols are bandwidth-constrained, difficult to scale and lack common security requirements. That makes it difficult to deliver enough bandwidth and compute power to vehicle components for reliable defense, and this is especially important when you talk about moving target defense.

As the US Defense Department steps up research into automated and autonomous vehicles, Army researchers are developing a way to enhance their internal security without undermining performance. Their new machine learning-based framework is designed to augment the security of in-vehicle computer networks.

Researchers from the Army Research Laboratory (ARL) in collaboration with international experts devised a technique to optimize a recognized cybersecurity strategy known as the moving target defense (MTD), which systematically changes multiple system dimensions to increase uncertainty and create complexity for attackers.

DESOLATOR provides deep reinforcement learning-based resource allocation and moving target defense deployment framework. It uses machine learning to help the in-vehicle network identify the best way to shuffle the frequency and bandwidth allocation of IP addresses to deliver effective, long-term moving target defense. When you shuffle the IP addresses fast enough, then the information assigned to the IP quickly becomes lost, and the adversary has to look for it again.

The new technology not only defends vehicle networks, but it also does so without generating additional overhead that could slow or degrade performance. It uses fewer resources to protect mission systems and connected devices in vehicles while maintaining the same quality of service, according to a program official.

The research team used deep reinforcement learning to shape the behavior of the algorithm so it would learn to limit exposure time and the number of dropped packets, for example. As a result, DESOLATOR identifies the optimal amount of network resources that should be allocated each network slice to minimize packet loss, according to

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