In the ever-changing digital world, data generation has skyrocketed. People are producing a lot of data every day on everything from PCs to smartphones. Over the following five years, up to 2025, Statista projects that the amount of data created globally will reach more than 180 zettabytes. Big data is the term used to describe this vast amount of data. Big data has various applications, particularly in the area of cyber security, despite the fact that it can be challenging to handle.

In this blog post, we address the questions of how big data is applied to cyber security and how actionable insights from big data analytics can improve the state of the art cyber security platforms. We also examine the ways in which several VaporVMs cyber security solutions have strengthened security by leveraging big data. Numerous real-world case studies demonstrate how big data has altered security procedures, from network and endpoint detection and response to continuous threat monitoring.

Why is big data analytics critical to cyber security?

Thus, how can cyber security be enhanced by big data? In order to maintain network security, cyber security depends on real-time analysis, detection, and patterned behavior. Large amounts of data can supply all this important information. In order to deliver the most complete security solutions, users can perform extensive data analysis by utilizing cyber security and data analytics platforms.

Big data cyber security is crucial for thwarting ever-changing potential threats, preventing illegal access, and spotting patterns that can lessen the impact of an assault. Deep learning, artificial intelligence, and other techniques are used by the framework to focus searches and identify dangers more quickly. Enhancing your cyber security posture using data analytics can guarantee effective, dependable, and consistent defense.

By utilizing data-driven cyber security solutions, you can protect your infrastructure against ever-changing threats and gain real-time insight on how to defend against them. Big data in cybersecurity, like that seen in VaporVM Endpoint Secure, can assist in spotting malware, insider threats, and zero-day assaults in a system before any harm is done. Network monitoring, cloud security, endpoint security, threat detection, and other areas are a few instances of this.

But utilizing big data to maintain good cyber security might be difficult given the volume of data to sort through. For successful security, the architecture must therefore guarantee smaller, actionable insights. Let’s investigate further how actionable insights from big data might improve cyber security.

Actionable Insights Are Made Possible by Big Data Cyber Security

As per Tech Target, conclusions derived from data that can be immediately translated into an action or a reaction are known as actionable insights. Big data analytics contains vast amounts of data, making it impractical to simply sort through it all in search of useful cyber security solutions. For this reason, the UK National Cyber Security Center suggested using quick fixes as a catalyst for change. Conclusions from the enormous collection of big data that may be used to guide specific actions or reactions are known as actionable insights.

Big data analytics can be utilized to simply provide these actionable insights or conclusions to influence concrete decisions. Many firms may believe that utilizing big data in cyber security necessitates a total revamp of current operations. The structured and unstructured data will be examined by analysts to determine what practical steps may be taken to guarantee efficient security.

Security analysts can then use these actionable information to enhance other business divisions. The company can include internet payment portals, for instance, if data from customer reviews indicates that 70% of customers prefer online transactions. Big data analytics does this by leveraging actionable insights to motivate internal change. The following are some examples of practical insights that big data can provide for cyber security:

  • monitoring of network traffic
  • examining data to find irregularities
  • Identifying malware patterns to improve threat detection
  • Analysis of behavior
  • Analysis of Artificial Intelligence (AI)
  • Filtering web pages using past patterns
  • Quick incident reaction in the event of security breaches

One simple approach to make sure your security systems are supported by intelligence, data science, and strong proof is to use big data for cyber security.

Cybersecurity Using Big Data Analytics Through Response and Detection on Networks

Network Detection and Response (NDR) technologies employ sophisticated analytical techniques, such as machine learning, that are not relying on signatures to identify potentially suspect network behavior. These systems continuously examine traffic metadata or raw network packets traveling between public (North-South) and internal (East-West) networks.

By simply entering the IP addresses, domains, ports, or URLs, Cyber Command’s Golden Eye functionality distinguishes itself from others by offering security teams a comprehensive and easily understandable graphical depiction of the assault chain at every stage of the cyberattack. This provides you with precise details on thorough root cause analysis, attack sources, entry point tracking, and appropriate and successful response strategies.

Detection of Blind Spots: VaporVM’s Cyber Command solution also addresses the problem of blind spots inside a network. Many businesses frequently have huge blind spots that limit their ability to view laterally across the network. East-West and North-South traffic visibility is fully and comprehensively provided by the Cyber Command platform. This provides users with unrestricted visibility to track, identify, and neutralize cyberthreats instantly.

In order to extract metadata, including source and destination IP addresses, protocols, ports, packet sizes, timestamps, and other network-level data, VaporVMs will gather raw network traffic mirrored from switches. They will then correlate the data into contextualized event logs so that Cyber Command can conduct a thorough analysis.

Platform Neural-X: Furthermore, VaporVM will guarantee cutting-edge cyber security using its highly sought-after Neural-X technology. Artificial intelligence (AI) powers the cloud-based threat intelligence and analytics functionality, which is constantly updated with real-time threat information of dangerous patterns and behaviors from several, reliable sources. VirusTotal, IBM X-Force, and other

Using NDR Case Studies to Apply Big Data Analytics to Cybersecurity: Hardware Supplier for Smart Cars
This maker of smart cars discovered that it was frequently the target of data-stealing cyberattacks. Unfortunately, because of inadequate internal network visibility and detection, the organization was unable to identify any network threats. When VaporVM’s Cyber Command was activated, it detected numerous anomalous access warnings right away, indicating that multiple internal network hosts were attempting malevolent DNS access requests. With broad visibility and big data analytics at its disposal, Cyber Command was able to determine the subnets and systems originating from the queries in real time.

Although big data analysis can be effectively included into your cyber security plan using NDR technologies, most cyber dangers really originate at the network’s edges. We’ll now look at how Endpoint Detection and Response solutions can be used to apply big data analytics for cyber security.

Cybersecurity Using Big Data Analytics Using Endpoint Identification and Reaction

The locations in a network where data is received or sent are known as endpoints. As a result, it ranks among a network’s most susceptible points. It is anticipated that the endpoint security industry will grow to about US$ 13.4 billion by 2023. It is not surprising that endpoint detection and response systems opt to use big data analytics in order to offer all-encompassing security. Platforms for endpoint detection and response will search all incoming and outgoing traffic for particular kinds of data, such as:

Procedures Documents Links
Users’ Systems
The gathered data will subsequently be utilized by VaporVM’s Endpoint Secure technology to guarantee comprehensive and uniform data throughout a network that is now fully viewable. The platform protects the network from all varieties of ransomware by combining static and dynamic AI-based detection engines. These engines block ransomware in less than three seconds, minimizing harm. Endpoint Secure uses big data to collect ransomware indicators of compromise from over 12 million devices that use the platform. This makes it possible for the approach to attain a 99.83% detection accuracy rate.

In order to provide thorough protection, the Endpoint Secure system focuses on three stages of a cyberattack:

Pre-Attack: This stage uses vulnerability and patch management, endpoint discovery, unified endpoint management, and baseline check configuration with an emphasis on prevention.
Passive detection and active protection are both essential components during an attack. Here, sandboxing, AI and machine learning, behavior-based detection, signature-based detection, and other passive detection characteristics are applied. Micro-segmentation, a ransomware honeypot, two-factor authentication, and the ability to detect brute-force attacks are examples of active defense against an assault.
Post-Attack: In the latter stages of an attack, Endpoint Secure will examine forensic analysis and residual threat detection, which include components of threat hunting, threat correlation, and threat visualization, respectively.
VaporVM’s Solution for Ransomware, which includes the recently launched Endpoint Secure 6.0.2 and Network Secure firewall, focuses on threat hunting to identify the APT that successfully infected your company since it understands that detection and blocking are the best approaches.

The platforms for continuous threat detection in cyber security also make big data analytics very useful. Now, let’s take a look at a few of the ways that VaporVM’s threat detection platforms have used big data analytics to proactively seek out security vulnerabilities.

Using Big Data Analytics to Continuously Detect Threats in Cybersecurity

Investing in a Continuous Threat Detection platform is essential for your cyber security in an ever-changing world of cyber threats that are resistant to manipulation and constant change. Your security measures must change as threats do to accommodate newer ones.

Since it only takes one data breach to disrupt a network, VaporVM is a firm believer in developing integrated threat-hunting solutions as part of a coordinated reaction to every incident.

Cyber Guardian and VaporVM’s Continuous Threat Detection operate together and correlate with big data analytics to offer a proactive, seamless threat-hunting environment that protects your network from both new and old malware.

Big data in cyber security enables VaporVM’s sophisticated and specialized platforms to do effective real-time analysis, improving protection and raising visibility for all users.

Cybersecurity: Selecting Big Data Analytics

The field of big data analytics in cyber security is expanding. More platforms and services in the sector must benefit from big data cyber security as technology and cyber threats advance. Big data can be utilized to gather useful information that will help develop workable cyber security solutions. As we’ve seen, use cases for continuous threat detection, endpoint detection and response systems, and network detection and response platforms have all made use of big data in cyber security.

Integrating cyber security and big data guarantees all-encompassing solutions for many businesses. VaporVM is committed to use big data analytics on all platforms to further extend its industry-leading cyber protection portfolio. Get in touch with VaporVM right now to learn more about our extensive selection of cyber security and cloud computing platforms, or go to to discover how big data analytics in cyber security is reshaping the sector.