Press release

Tested and Proven by SE Labs Threat Prevention Evaluation Test: Deep Instinct Prevents Today’s Most Sophisticated File-based and File-less Attacks

Sponsored by Businesswire

According to the latest test
results from SE Labs’ independent threat prevention evaluation lab
Deep Instinct’s D-Client (v2.2.1.5) achieved a 100% prevention rate and
zero false-positives – when detecting and blocking known and unknown
cyber threats, including file-based and file-less attacks. The
independent results also highlighted Deep Instinct’s ability to provide
a wide range of detection and threat blocking capabilities without
interfering with system performance.

Powering Deep Instinct’s D-Client is a deep learning-based malware
detection and prevention engine, D-Brain, which was trained in August
2018, six months prior to the customized-targeted threats being created.
However, D-Client was still able to successfully detect and prevent all
attacks. By leveraging D-Brain’s proprietary deep neural network
architecture – revered for the best accuracy in known and unknown
malware detection and prevention – all threats were successfully
prevented pre-execution with no other processes running.

“SE Labs’ tests are renowned for being technically challenging and
in-depth. It’s rare to achieve 100 percent ratings, which is precisely
what Deep Instinct has done with its D-Client solution,” said Simon
Edwards, CEO of SE Labs and chairman of the board of the Anti-Malware
Testing Standards Organization (AMTSO). “It’s impressive that the
company’s technology was capable not only of blocking all of the
advanced threats, but to do so with zero-false positives.”

As the first company to apply deep learning to cybersecurity, Deep
Instinct leverages the power of deep learning’s predictive capabilities
to create the ultimate zero-time threat prevention platform and network,
which involves multi-layer protection across all endpoints, servers,
mobile devices and operation systems (Windows, macOS, Android and
ChromeOS) to guard against zero-day threats and APT attacks with
unmatched accuracy.

“Whether it’s typical ransomware or not, attack vectors are constantly
changing, and without the correct tools to tell you when a malicious
attack will occur, deep learning-based cybersecurity is the only answer
to best protect against the known and unknown,” said Guy Caspi, CEO and
co-founder of Deep Instinct. “The results speak for themselves and are
indicative of our mission and commitment to keep customers safe from a
multitude of cyber threats.”

The test comprised four main categories of attack: known, public malware
campaigns; script-based targeted attacks, e.g. file-less, designed to
avoid interacting with the hard disk of target systems; targeted attacks
based on macros and vulnerabilities in Microsoft Office file-formats,
e.g. ‘client-side attacks, and shellcode injection attacks designed to
inject malicious code into legitimate software.

To effectively test D-Client software, SE Labs collected malware from a
range of well-known breaches, including attacks from APT28 (Fancy Bear)
that originally targeted the U.S. presidential election of 2016; APT29
(Cozy Bear) which was believed to be behind the compromise of the U.S.
Democratic National Committee in 2015 and a banking Trojan designed to
steal personal details – identified by the U.S. Department of Homeland
Security and many others. The samples that were tested were new variants
of those campaigns, which were never available in the wild before.

In addition to the known ‘public’ threats, SE Labs testers also
generated a range of advanced targeted attacks using known malicious
techniques to create new, unknown files. These included malicious
Microsoft Office and JavaScript files, which were included to explore
whether D-Client could handle malware of which it lacked prior knowledge
and file-less attacks. Not only did D-Client detect all the public or
“known” attacks, it successfully protected against every one of the new
Microsoft Office-related attacks launched. It also went a step further
to protect systems from any ill-effects, such as infection from
ransomware and theft or data destruction.

“The number of modern threats has grown significantly over the years,
just as their behavior has also changed, leveraging automation and
AI-like techniques for maximizing their impact and velocity”, said
Fernando Montenegro, a senior industry analyst at 451 Research. “That
said, the same type of machine learning/AI techniques can and have been
employed by defenders with great success. As organizations look at their
options for building security defenses, test results by external labs
are one of the significant signals to consider in their selection

For more information about Deep Instinct and its deep learning-based
cybersecurity, visit:
Download the full SE Lab report here.

About Deep Instinct

Deep Instinct is the first company to apply deep learning to
cybersecurity. Deep learning is inspired by the brain’s ability to
learn. Once a brain learns to identify an object, its identification
becomes second nature. Similarly, as Deep Instinct’s artificial deep
neural network brain learns to prevent any type of cyber threat, its
prediction capabilities become instinctive. As a result, any kind of
malware, known and new, first-seen malware, zero-days, ransomware and
APT attacks from any kind are predicted and prevented in zero-time with
unmatched accuracy and speed anywhere in the enterprise – Network, EPP,
Mobile – enabling a multi layered protection. To learn more, visit: