Deep Instinct has developed the first deep learning cybersecurity framework, and studies show it has a 100 percent threat detection rate.
On Wednesday (May 16), Deep Instinct partnered with Tech Data (NASDAQ: TECD ) to make its deep learning cybersecurity framework available for Tech Data’s products.
Tech Data has operations in over 100 countries and in March it reported US$37.5 billion in net sales for the 2019 fiscal year. As part of the collaboration, Tech Data will offer clients in Latin America, Canada and the US Deep Instinct’s cybersecurity framework.
Deep Instinct has developed a deep learning framework designed specifically for cybersecurity, which it says is the first framework of its kind.
“Our customers are always looking for the best, most advanced solutions to support their security needs, and, based on what we have seen from Deep Instinct, our customers are going to appreciate the value this solution will bring to their organizations,” said Yuda Saydun, president of CyVent, Tech Data’s channel partner, in a press release.
For Deep Instinct, the partnership will allow it to leverage the scale and relationships of Tech Data to further extend its deep learning product.
“Deep learning is much different than what’s out there in the market,” Grady Johnston, vice president of channel and alliances at Deep Instinct, said via phone. “A lot of it is machine learning, where you still need people in the background telling the application what to look for and what to change.”
Instead, Deep Instinct feeds computers files, which the computers learn to identify on their own.
“With deep learning, it’s about what we call a brain, where we actually create what we call our own little university with multiple huge computers,” Johnston explained. “We feed it tons of millions and billions of files of good and bad, and this brain learns what’s good and bad by the bytes.”
Results from SE Labs have shown 100 percent threat detection and prevention rates from Deep Instinct’s framework for identifying malware and viruses.
“So no matter what attack that comes or has been mutated, our brain will see it because it looks at every little byte, so it stops it before execution. We are a prevention company,” said Johnston.
Where deep learning is distinguished from machine learning is that machine learning requires feature extraction, which is when humans tell the computer what different characteristics or properties are.
Take, for example, classifying spam emails. Specific features are tied to different types of emails and through machine learning, the computer learns to automatically file spam emails into a separate folder. The same process has been applied to image recognition, voice recognition and diagnosis.[…]