Critical Infrastructure Cybersecurity: How to Align with Tier 4 NIST Framework Guidelines
With the environment, high-value assets, and even human lives at stake, strong critical infrastructure cybersecurity couldn’t be more vital.
Cyberbit, a CyVent partner, is helping organizations adhere to the NIST Framework for Improving Critical Infrastructure Cybersecurity with its Cyberbit SCADAShield.
Though IP connectivity has brought huge operational advantages to critical infrastructure organizations, it has also opened the doors to external threats that air-gapped ICS networks weren’t worried about previously.
The NIST Framework is the industry standard for ICS cybersecurity. With the Cyberbit SCADAShield, organizations can implement the NIST Framework at Tier 4 — the highest level of adaptive security.
According to NIST, “behavioral anomaly detection technology can provide a key security component in sustaining business operations, particularly those based on ICS.”
SCADAShield platform uses ICS-specific behavioral analytics and machine learning to rapidly detect anomalous behavior, including:
- Continuous ICS threat monitoring
- Asset discovery
- Considerations for the Financial Services Security Team
- Threat intelligence
- Adaptive risk management
Aligning with the NIST framework at the highest security level requires new technology solutions. In this white paper, learn more about how you can use Cyberbit behavioral analytics and machine learning technology to help keep your ICS network secure in the age of connectivity.
Explore the Challenges & Future of Cybersecurity in Demisto’s State of Incident Response Report
Technological advancements are making our work and lives easier. But securing innovative new tools? That’s never been more difficult.
Security teams continue to be overworked and understaffed, even as cybersecurity demands greater attention.
Demisto’s “The State of SOAR,” their second annual state of incident response report, takes a comprehensive look at the greatest points of concern for security teams, including:
Rising alert volumes
The cyber talent shortage
Piecemeal processes and measurement
The report also takes a look into the future, diving into solutions that have the potential to address our biggest cyber challenges, including:
- Threat hunting
- Automation tools
- Machine learning solutions
What challenges is your security team facing? Reach out to us with any questions or concerns surrounding incident response. CyVent is an authorized Demisto partner, and we’re here to help you work through today’s challenges and prepare for the future.
Detect Threats Already Inside: Machine Learning in Cyber Security
Our approach to cyber security needs a refresh. Despite all the attention lavished on keeping bad actors out, headlines show us every day that the perimeter is a sieve.
To better secure a network, we need to add another toolset that can catch stealthy attackers who avoided the endpoint defenses.
Malware can lay dormant for months, external hackers hide in plain sight, and the risk of an internal attack can’t be underestimated. Using machine learning for cyber security gives businesses the ability to detect if their organization has been infiltrated in ways traditional tools can’t.
Darktrace, a CyVent certified partner, protects companies from silent, stealthy attacks through a combination of unsupervised machine learning and Bayesian mathematics.
Like the human immune system attacks foreign bodies that present a threat to wellbeing, Darktrace’s Enterprise Immune System does the same for organizations. By learning what is normal for an organization and its people, Darktrace is then able to spot anomalies within the environment, detecting previously unknown and in-progress attacks.
Businesses need tools to intelligently monitor their networks and automatically respond to the most serious cyber threats, especially ones that have already made it past the perimeter. Unsupervised machine learning in cybersecurity equips businesses to fight back. Download the white paper from Darktrace to learn more.
Have questions about Darktrace’s Enterprise Immune System technology? We’re available with expert advice to help you evaluate if their solution is the best fit for you. Reach out to one of our advisers.
Topics: Machine Learning
Security Solutions for MSSPs in Multi-Tenant Environments
Advanced cyber attacks continue to be more prevalent with increased sophistication and are indiscriminately targeting industry sectors and organizations of all sizes.
It is especially becoming more difficult for small to mid-size organizations with limited resources to manage, monitor, and respond to advance security threats by themselves.
Because of this, organizations are becoming more reliant on Managed Security Service Providers (MSSP), who have proven technology that protects from zero-day ransomware, malware, and APT threats.
In this white paper, "Multi-Tenancy Security Solution for MSSPs" from Deep Instinct, you will learn about:
- Why there is an increased need for MSSPs to provide advanced endpoint security services
- What is required from an advanced EPP/EDR product to be managed by MSSPs
- What can be further provided by security vendors for multi-tenant management
If you have questions about MSPPs can provide your organization with advanced security services, we’re available with expert advice. Reach out to one of our advisors.
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.