A handful of government agencies think next-generation cybersecurity will work like the human brain — or better.
The Defense and Energy departments are among a group of federal organizations that think brain-inspired nanotechnology could help the government protect its networks, according to a white paper published in July.
Eventually, this technology could help agencies prevent unauthorized access to networks, pick up on anomalous user behavior and help with situational awareness, the paper said.
These nanoscale developments, which might involve combining biological with synthetic material, could also help organizations monitor energy or weapons systems "that require software (or combination of multiple codes) so complicated that it exceeds a human’s ability to write and verify the software and its performance," the paper said.
Scientists also could use this technology to quickly create personalized treatments for individual patients, build more complex networks, or eventually allow "advanced robots to work safely alongside people."
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Last October, the White House announced a nanotechnology-inspired Grand Challenge, which asked researchers to "[c]reate a new type of computer that can proactively interpret and learn from data, solve unfamiliar problems using what it has learned, and operate with the energy efficiency of the human brain.”
The agencies writing the white paper outlined several timeline benchmarks for nanotechnology development: They anticipate achieving "autonomous capabilities" for routine cyberattacks in the next five years, and the same for sophisticated attacks in the next decade. In 15 years, they expect nanotechnology will evolve to the point where it can help agencies respond to sophisticated attacks "with compact and energy-efficient computing resources.”
The agencies would like scientists to be able to "[t]ranslate knowledge from biology, neuroscience, materials science, physics and engineering" into information that computer system designers can use, over the next five years; also to create programming languages that "do not require deep knowledge and expertise to use."
The ultimate goal is to "minimize expert knowledge requirements in materials or device physics" needed to create new brain-inspired computing systems, the white paper said.