Neutralize Cyber Threats with Darktrace’s Unsupervised Machine Learning Technology

Unsupervised machine learning technology is bringing about a new age of cyber defense. Traditional defenses are known to secure against threats that are known, but can’t stop previously unseen ones. Once past perimeter defenses, these emerging threats usually remain active inside the network for extended periods of time and are near-impossible to detect. Unsupervised machine learning technology is now empowering companies to neutralize never-before-seen threats in real time.

CyVent is a Certified Partner of Darktrace, a global leader in machine learning applied to cybersecurity, whose technology can detect and autonomously respond to cyber threats that legacy systems miss. Their “‘Enterprise Immune System” technology has been deployed at thousands of organizations worldwide and leverages unsupervised learning to fight back against cyber threats as they unfold in real time.

• Insider threats – malicious or accidental
• Zero-day attacks – previously unseen, novel exploits
• Latent vulnerabilities – dormant vulnerabilities that are undiscovered, often due to the lack of network visibility
• Machine-speed attacks – ransomware and other automated attackers that propagate and/or mutate very quickly and are virtually impossible to stop and neutralize using human-dependent response mechanisms
• Silent and stealthy attacks that lurk in networks undetected

This white paper provides insight on why legacy systems are leaving companies exposed and outlines a unique approach to security, which combines unsupervised learning and deep learning for some of the strongest defenses.

Though machine learning in cybersecurity is not uncommon, most solutions rely on a supervised approach that requires knowledge of past attacks. Darktrace’s unsupervised machine learning identifies trends in data, without human input, to stay up to date and detect even the most innovative attackers.

From ransomware to data breaches to attacks against the IoT and cloud, Darktrace spots anomalies and prevents attacks from spreading before they turn into a devastating security breach.

Prevention is the best cure for unknown cyber threats. 

And in the world of endpoint security, prevention is making a comeback.

As cyber criminals became more sophisticated, organizations turned to detection and response to keep their network and endpoints secure. Existing prevention tools simply weren’t strong enough to catch unknown threats.

But with the advent of deep learning, an advanced form of artificial intelligence, organizations can once again embrace prevention solutions to detect never-before seen malware, zero-day, ransomware, and APT attacks.

70% of successful breaches originate from the endpoint, making the need for improved endpoint security solutions critical. Deep Instinct, a CyVent certified partner, is pioneering the application of deep learning for cyber security.

In this white paper, “Reinventing Cybersecurity Prevention with Deep Learning” from Deep Instinct, you will learn about:

• The evolution of endpoint security, from the antivirus era to behavioral analysis and deep learning
• Why the prevention approach to endpoint security has returned and why it is important
• Must-have requirements for achieving real-time unknown threat prevention

If you have questions about deep learning for cyber security, we’re available with expert advice to help you evaluate if the solution is the best fit for you. Reach out to one of our advisors.

Using Anti-Evasion Malware Detection Techniques to Block Stealth Attacks: SANS Product Review on Minerva Labs

In cybersecurity, the pressure is always on. Securing your network is an ongoing struggle and deploying an array of security tools often results in more alerts than you can handle. When alerts pile up, they create a bigger headache instead of fixing issues and detecting threats as intended.

Anti-evasion technology is helping organizations avoid the overlapping noise of alert upon alert. While traditional defenses scan AntiVirus files to evaluate threats, Minerva Labs uses advanced malware detection techniques to outsmart malware by tricking it into attacking itself.

SANS, a leading cooperative research and education organization for security professionals, tested Minerva’s anti-evasion software to see how it would hold up in crisis.

“Most endpoint security solutions focus on examining file attributes or behavioral patterns of how malware operates,” SANS reported. “Therefore, as the malware becomes more evasive, the effectiveness of the techniques deteriorates rapidly. In contrast, with Minerva’s Anti-Evasion Platform, the more evasive the malware we tested, the more effective the solution was at preventing the threat from affecting the system.”

In their review, SANS ran multiple attack types against Minerva, including malware with the following criteria:

• Sandbox avoidance
• Memory injection attacks
• Use of malicious documents
• File destruction

Find out more about how the Minerva malware detection platform performed when it went head-to-head with each of these malicious attacks.