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Deep Instinct Files Portfolio of Patents to Apply Deep Learning to Cybersecurity

Startup company Deep Instinct announced last week that it has filed five patents covering the only technology that applies deep learning to cybersecurity. The extensive patent applications, which contain, in the aggregate, over 150 independent claims, cover the entire body of knowledge achieved by Deep Instinct in deep learning and cybersecurity: both for currently commercially available products and for future planned products.

As a branch of artificial intelligence, deep learning is considered by many researchers in the field of computational intelligence to contain the most suitable family of algorithms for domains that require the ability to analyze vast and complex data, such as the data comprising files in cybersecurity.

Deep Instinct is the first company applying deep learning to detect, in real-time, malware on endpoints, servers and mobile devices, focusing on zero days and APT attacks – areas where traditional cybersecurity practices lack the capacity to protect in real-time.

In order to obtain a trained deep learning model, the neural networks comprising the deep learning must be trained on a large corpus of data. Following its training phase, the deep learning model can then operate in a prediction mode. Applying deep learning to both phases – training and prediction – required Deep Instinct to develop tailored methodologies to optimize the detection rate, as well as other relevant parameters, such as speed and consumption of resources. The patent applications, which are confidential, use deep learning libraries and algorithms that were created by Deep Instinct and cover the novelty of Deep Instinct’s technology in these operational phases of the deep learning model. They relate to Deep Instinct’s ability to condense its deep learning model into a small, light agent that is installed on any endpoint, server and mobile device and operates in an autonomous manner to detect and prevent any type of malware.

Deep learning facilitated further ingenuity that led to filing one of the five patents that pertains to traffic. In an unsupervised form, deep learning is considered the best methodology for identifying anomalies, minor mutations and non-regular activities. These are areas where traditional cybersecurity approaches lack the capacity to provide comprehensive, real-time protection. Deep Instinct has claimed patent protection with respect to its capability to apply deep learning to Deep Packet Inspection (DPI) and Packet Analysis to detect these phenomena in the network of any enterprise or other entities (including smart vehicles) from raw data algorithms.

“The remarkable technological capabilities of deep learning and our particular expertise enabled us to create an extensive portfolio of unique patents in cybersecurity, which to the best of my knowledge, make Deep Instinct the first company to file such a broad range of this type of patents.” said Guy Caspi, CEO of Deep Instinct. “I am proud to share this complex and lengthy process that we have undergone, which spanned over a year, starting in 2015 and ending when we made the patent applications.”

Deep Instinct is headquartered in Tel Aviv, Israel and has offices in San Francisco, Calif.

Additional information related to this topic and cybersecurity jobs can be found here.

Article published by Anna Hill
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