The researchers in the United States have developed a new machine learning tool, which they are calling the ‘DESOLATOR’ that has been mainly designed for the purpose of securing the networks that are running a vehicle preventing the vehicle from being hacked, along with not impacting the performance.
This technology has been designed by the United States Army in the partnership with the University of Queensland, along with Virginia Tech and the Gwangju Institute of technology that is going to help the US Army for the purpose of optimizing the cybersecurity for moving the vehicles that are mainly in the target of being attacked or hacked.
The DESOLATOR is an abbreviation for deep reinforcement of learning-based resource allocation and moving target defense deployment, and this technology provides help to the network inside the vehicle for the purpose of identifying the allocation of bandwidth and IP shuffling frequency for the purpose of delivering the effective moving target of defense.
Dr. Terrance Moore, a mathematician with US Army said, the idea behind the ‘DESOLATOR’ is that, it is tough for hitting a moving target, and if everything is static, the adversary can possibly be responsible in taking more time in focusing at everything and choosing the targets, but if the IP addresses are shuffled, then the data or information assigned to the address will become lost quickly.
The development team has used a technique of Artificial Intelligence for the purpose of calling the deep reinforcement learning for the purpose of shaping the behavior of the algorithms gradually.