Deep Instinct Raises $43 Million For Deep Learning To Thwart Cyberattacks
Unlike traditional security solutions that primarily guard against known threats in the Windows operating system and help identify a cyberattack once it has already breached a system, Deep Instinct uses a patented deep learning platform trained to identify and prevent first-seen, sophisticated and advanced cyber threats. Threats are prevented anywhere within the enterprise from any type of file-based or file-less cyber attacks in zero-time, with unmatched accuracy and speed.
Deep Instinct raises $43 million for deep learning to thwart cyberattacks
In the case of Deep Instinct, the company says its deep learning platfrom can identify new forms of attacks based on its experience fighting previously known infiltrations. By automating analysis of attacks such as malware, the company says it can more effectively protect enterprise networks, endpoint devices, and different operating systems.
Deep Instinct, which uses deep learning both to learn how to identify and stop known viruses and other hacking techniques, as well as to be able to identify completely new approaches that have not been identified before, has raised $43 million in a Series C.
Based on a purpose-built deep learning framework for cybersecurity, the Deep Instinct platform can predict, prevent, and analyze cyberattacks at any touchpoint of the organization from the endpoint through to the network.
He added, "As we enter a new phase of hyper-growth, this investment round will significantly expand our go-to-market capabilities while at the same time increase our best-in-class deep learning research and product development groups. These groups will focus on further developing the company's unique deep learning platform beyond endpoint into cloud, network, and storage to meet the accelerating needs of our customers in the face of more sophisticated threats and breaches."
Deep Instinct chairman Lane Bess said, "After 20 years of bringing early-stage companies to public market entry and having been involved with Deep Instinct since inception, I can say with certainty that the benefits of our deep learning technology will change how the industry looks at cybersecurity. I see our platform emerging as an essential security component in the next few years. With the support of our investors, Deep Instinct will continue to grow as the only company to develop deep learning cyber prediction and prevention capabilities - and essentially vaccinate enterprises from cyber vulnerabilities."
What it does: To identify and stop cyberattacks, Deep Instinct uses deep learning, a field of machine learning that allows computer programs to imitate the processes of a human brain to spot patterns.
Founded in 2015, Deep Instinct has experienced rapid growth and has raised over $92 million in funding. Last year, the company exposed one of the largest data breaches in the last decade. More than 250 million government, corporate, and private users were impacted by nefarious malware, TrickBooster, which was stealing sensitive financial data. More recently, the company has contracted with T-Systems to continue its strategic EMEA expansion. Led by a highly experienced and interdisciplinary team of deep learning scientists and Ex-IDF Intelligence cyber units, this combined force is transforming the cybersecurity sector.
Intezer uses its core Genetic Malware Analysis technology to focus on cyber threat detection and response. It is unique in that it also provides companies with deep context in order to determine the best response strategy. The platform targets incident response automation, cloud workload protection, and threat intelligence, among other features. This allows companies to detect and classify cyberattacks by identifying the software responsible for the threat. This then makes it much harder for criminals and bad actors to launch new attacks.
Nvidia's GPU Technology Conference (GTC), now called Nvidia GTC, is a series of technical conferences held around the world.[98] It originated in 2009 in San Jose, California, with an initial focus on the potential for solving computing challenges through GPUs.[99] In recent years, the conference focus has shifted to various applications of artificial intelligence and deep learning, including: self-driving cars, healthcare, high performance computing, and Nvidia Deep Learning Institute (DLI) training.[100] GTC 2018 attracted over 8400 attendees.[98] GTC 2020 was converted to a digital event and drew roughly 59,000 registrants.[101]
Nvidia GPUs are used in deep learning, and accelerated analytics due to Nvidia's API CUDA which allows programmers to utilize the higher number of cores present in GPUs to parallelize BLAS operations which are extensively used in machine learning algorithms.[7] They were included in many Tesla vehicles before Elon Musk announced at Tesla Autonomy Day in 2019 that the company developed its own SoC and full self-driving computer now and would stop using Nvidia hardware for their vehicles.[121][122] These GPUs are used by researchers, laboratories, tech companies and enterprise companies.[123] In 2009, Nvidia was involved in what was called the "big bang" of deep learning, "as deep-learning neural networks were combined with Nvidia graphics processing units (GPUs)".[124] That year, the Google Brain used Nvidia GPUs to create Deep Neural Networks capable of machine learning, where Andrew Ng determined that GPUs could increase the speed of deep-learning systems by about 100 times.[125]
In April 2016, Nvidia produced the DGX-1 based on an 8 GPU cluster, to improve the ability of users to use deep learning by combining GPUs with integrated deep learning software.[126] It also developed Nvidia Tesla K80 and P100 GPU-based virtual machines, which are available through Google Cloud, which Google installed in November 2016.[127] Microsoft added GPU servers in a preview offering of its N series based on Nvidia's Tesla K80s, each containing 4992 processing cores. Later that year, AWS's P2 instance was produced using up to 16 Nvidia Tesla K80 GPUs. That month Nvidia also partnered with IBM to create a software kit that boosts the AI capabilities of Watson,[128] called IBM PowerAI.[129][130] Nvidia also offers its own Nvidia Deep Learning software development kit.[131] In 2017, the GPUs were also brought online at the Riken Center for Advanced Intelligence Project for Fujitsu.[132] The company's deep learning technology led to a boost in its 2017 earnings.[133]