New brain-inspired cybersecurity system detects 'bad apples' 100 times faster
Sophisticated cybersecurity systems excel at finding "bad apples" in computer networks, but they lack the computing power to identify the threats directly.
Instead, they look for general indicators of an attack; call them "apples." Or the system flags very specific patterns, such as "bad Granny Smith apples" or "bad Red Delicious apples."
These limits make it easy for new species of "bad apples" to evade modern cybersecurity systems. And security analysts must sort the real dangers from false alarms, such as the nonsense phrase "forbad applesauce."
The Neuromorphic Cyber Microscope, designed by Lewis Rhodes Labs in partnership with Sandia National Laboratories, directly addresses this limitation. Due to its brain-inspired design, it can look for the complex patterns that indicate specific "bad apples," all while using less electricity than a standard 60-watt light bulb.
From cerebral palsy to a cybersecurity system
The processor in the Neuromorphic Cyber Microscope is based on the neuroscience research of Dr. Pamela Follett, a co-founder of Lewis Rhodes Labs. Follett is a pediatric neurologist and neuroscientist who studies developmental diseases, such as cerebral palsy in children. Her husband, David Follett, co-founder and CEO of Lewis Rhodes Labs, used her work as the basis for a computational model of how the brain processes information.
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https://www.sciencedaily.com/releases/2017/03/170321122540.htm
