Big Data

Deep Instinct’s neural networks for cybersecurity attract $110M

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The increasingly rich data companies are collecting make them a more tantalizing target for attacks. Deep Instinct wants to turn that same data into an enterprise’s great defensive asset.

The company is applying end-to-end deep learning to cybersecurity. The approach allows the company to predict and prevent cyberattacks across a company’s network, according to Deep Instinct CEO Guy Caspi.

Today, the company announced it has raised $100 million in a round led by BlackRock. Other investors include Untitled Investments, The Tudor Group, Anne Wojcicki, Millennium, Unbound, and Coatue Management. The company has now raised a total of $200 million.

AI for security

The New York-based company is part of a growing wave of startups turning to machine learning and artificial intelligence to combat the rising number of cyberattacks. The industry is optimistic that this ability to automate defenses will help companies gain an edge against increasingly sophisticated and well-funded hackers.

In the case of Deep Instinct, the company is trying to go a step beyond the way others are using AI and machine learning for security. Deep Instinct has created deep neural networks which allow it to avoid using feature processing that can add an additional step that slows reaction time.

With traditional machine learning, Caspi explained, executable files cannot directly be processed. Instead, they must be converted into a list of features which are then fed into a machine learning model.

Deep Instinct’s end-to-end deep learning system uses the raw data as input without needing to convert it. The company trains its model in its own labs, rather than on the customer’s premises, by feeding it hundreds of millions of malicious and legitimate files. This use huge scale training relies on Nvidia GPUs to handle the load.

Once that training is finished, it creates a standalone neural network that can then be deployed to customers where it starts protecting every device connected to the network. Because the system doesn’t require agents, it can rapidly be installed, including covering all applications running. And it can recognized previously unknown types of attacks without needing to be constantly updated.

As a result, Deep Instinct claims it can identify and stop attacks within 20 milliseconds while reducing false positives by 99 percent

Next steps

Caspi said he wants to use the latest funding to accelerate growth with an eye toward an IPO in the next couple of years. For now, that means ramping up sales and marketing with about 30% of the money being reserved for product development.

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