What is Big Data Analytics for Cyber Security

What is Big Data Analytics for Cyber Security

In a dynamic digital world, the generation of data has increased exponentially. From smartphones to PCs, people are creating large amounts of data each day. According to Statista, global data creation over the next five years up to 2025 is projected to grow to more than 180 zettabytes. This large expanse of data is what we refer to as big data. While big data can be difficult to process, it has many use cases – especially in the field of cyber security. (Read more: Sangfor’s article on how to manage data explosion with software defined storage.)

In this blog article, we ask how big data is used in cyber security and how big data analytics can elevate existing cyber security platforms through actionable insights. We also explore the ways different Sangfor’s cyber security solutions have utilized big data to reinforce protection. From network detection and response, and endpoint detection and response, to continuous threat detection – big data has changed the way security is maintained in various real-life case studies.

Why is Big Data Analytics for Cyber Security is important?

So, how can big data improve cyber security? Cyber security relies on real-time analysis, identification, and patterned behavior to keep networks secure. All this crucial information can be provided by large volumes of data. Using data analytics and cyber security platforms gives users intensive analysis of a data set to provide the best comprehensive security solutions.

Big data cyber security is important to combat evolving potential threats and prevent unauthorized access by adopting innovative strategies and identifying patterns that can mitigate an attack. The framework adopts deep machine learning, AI, and other methods to narrow down searches and signal threats faster. Using data analytics to improve your cyber security posture will ensure efficient, consistent, and reliable protection.

Using data-driven cyber security measures also keeps your infrastructure secure from new and evolving threats while building intelligence on how to combat these attacks in real time. Big data in cyber security such as Sangfor Endpoint Secure can help identify zero-day attacks, insider attacks, and various malware within the system before any damage can be done. Examples of this include network monitoring, cloud security, endpoint security, threat detection, and more.

However, with such a large amount of data to store and process, maintaining effective cyber security using big data can be challenging. This is why the framework needs to ensure smaller actionable insights to provide effective security. Let’s explore more about how big data provides actionable insights that can enhance cyber security.

Types of Big Data Analytics

Big data analytics can be categorized into several types, each serving a unique purpose in analyzing and interpreting large datasets. Here are the main types of big data analytics:

1. Descriptive Analytics: This type focuses on summarizing historical data to understand what has happened in the past. It uses data aggregation and data mining techniques to provide insights into past trends and patterns.
2. Diagnostic Analytics: This type goes a step further by analyzing historical data to determine why something happened. It uses techniques such as drill-down, data discovery, data mining, and correlations to identify the root causes of events.
3. Predictive Analytics: This type uses statistical models and machine learning techniques to predict future outcomes based on historical data. It helps organizations anticipate potential threats and opportunities by identifying patterns and trends.
4. Prescriptive Analytics: This type provides recommendations for actions to achieve desired outcomes. It uses optimization and simulation algorithms to suggest the best course of action based on predictive analytics.
5. Real-Time Analytics: This type involves analyzing data as it is generated to provide immediate insights and enable quick decision-making. It is crucial for applications that require instant feedback, such as fraud detection and network security.
6. Exploratory Data Analysis (EDA): This type involves analyzing data sets to find patterns, relationships, or anomalies without having a specific hypothesis in mind. It is often used in the initial stages of data analysis to discover new insights.

In addition to these types, organizations often utilize a data warehouse to store and process large volumes of data efficiently. Furthermore, the rise of NoSQL databases has transformed how data is managed, allowing for more flexible data models that can handle unstructured data, such as that generated from social media platforms. Each type of big data analytics plays a vital role in helping organizations make informed decisions, improve operational efficiency, and enhance security measures.

Big Data Cyber Security Enables Actionable Insights

According to Tech Target, actionable insights are conclusions drawn from data that can be turned directly into an action or a response. Due to the large amount of data within big data analytics, it becomes impractical to simply sift through it all to find effective cyber security solutions. This is why the UK National Cyber Security Center noted that small actionable insights be used to drive action. Actionable insights are conclusions drawn from the vast pool of big data collected that can lead to direct actions or responses.

Many companies might be under the misconception that using big data in cyber security requires a complete overhaul of existing workflows, however, big data analytics can be used to simply generate these actionable insights or conclusions to inform tangible decisions. Data analyst and data scientists will go over the structured and unstructured data to see what real-life responses can be made to ensure effective security.

These actionable insights can then be used by security analysts to improve various sectors within a business. For example, if data from customer reviews reveal that 70% of customers prefer online transactions, the business can take the action of including online payment portals. In this way, big data analytics uses actionable insights to drive a response from within. Examples of actionable insights that big data can achieve for cyber security include:

• Network traffic monitoring
• Analyzing data to detect anomalies
• Malware pattern identification for better threat detection
• Behavioral analysis
• Artificial Intelligence (AI) analysis
• Web Page filtering based on previous patterns
• Prompt incident response for security breaches

By leveraging big data analytics, organizations can gain insights into past and present security events, helping to inform business decisions. The benefits of big data analytics in cyber security include improved threat detection and response times. Using big data for cyber security is an easy way to ensure that your security systems are backed by solid evidence, data science, and intelligence. Let’s take a look at how big data analytics can be leveraged for cyber security through a network detection and response platform.

Big Data Analytics for Cyber Security Through Network Detection and Response

Network Detection and Response – or NDR – tools use non-signature-based advanced analytical techniques like machine learning to detect suspicious network activity. These platforms continuously analyze raw network packets or traffic metadata between internal networks (East-West) and public networks (North-South). Watch this YouTube video to know more about What is Network Detection and Response.

Sangfor’s Cyber Command NDR solution uses superior threat detection to identify and respond to threats in real-time with the help of AI technology and behavior analysis – all through the use of big data analytics and global threat intelligence. Sangfor’s advanced machine learning software excels at providing comprehensive threat detection while impact analysis within the network tracks known breaches back to “patient zero” by evaluating all possible points of entrance.

• Sangfor’s Golden Eye: Cyber Command’s Golden Eye feature stands out with its unique ability to display every stage of a cyber-attack and give security teams a detailed and intuitive graphical representation of the attack chain by simply inputting the IP addresses, domains, ports, or URLs. This gives you accurate information about in-depth root cause analysis, tracking of entry points, sources of the attack, and how to respond appropriately and effectively.
• Blind Spot Detection: Additionally, Sangfor’s Cyber Command solution combats the issue of blind spots in a network. Often, many companies have gaping blind spots that prevent them from seeing laterally across the network. The Cyber Command platform provides complete and comprehensive visibility of both East-West and North-South traffic. This gives users unfettered visibility to monitor, detect, and mitigate cyber threats in real-time.
• Sangfor’s Stealth Threat Analysis - or STA – is another highlighted feature in the Cyber Command solution. While only 99% of malware can be blocked by most cyber security solutions, Sangfor goes after the 1% as well by using this STA sensor. Sangfor’s STA will collect raw network traffic mirrored from switches to extract metadata - such as the source and destination IP addresses, protocol, port, packet size, timestamps, and other network-level data – then correlate the data into contextualized event logs for Cyber Command to perform an in-depth analysis.
• Neural-X Platform: Moreover, Sangfor will use its coveted Neural-X platform to ensure advanced cyber security. The cloud-based threat intelligence and analytics feature is powered by AI and is continuously enriched with real-time threat intelligence of malicious patterns and behaviors from extensive and well-established sources. This includes VirusTotal, IBM X-Force, AlienVault OTX, EmergingThreats.net, Abuse.ch, and more. Sangfor’s Neural-X is a pool of big data used to combat cyber-attacks.

The raw data collected, curated, and analyzed by Sangfor’s Cyber Command has been used to secure vulnerable networks and prevent several cyber-attacks.

Big Data Analytics for Cyber Security through NDR Case Studies: Smart Car Hardware Vendor

This smart car manufacturer found itself continuously targeted by cyber-attacks in which data was stolen. However, the company could not find any network threats due to poor detection and visibility within the internal network. Sangfor’s Cyber Command was put into action and immediately picked up on a large number of unusual access alerts that showed several hosts on the internal network making malicious DNS access requests. With extensive visibility and big data analytics at hand, Cyber Command instantly identified the subnets and systems where the requests were coming from in real-time.

While NDR solutions are a great way to integrate big data analysis into your cyber security plan, the majority of cyber threats do emerge at the ends of a network. Now, we’ll explore how big data analytics can be implemented for cyber security through the use of Endpoint Detection and Response platforms.

Big Data Analytics for Cyber Security Through Endpoint Detection and Response

Endpoints are the points in a network through which data is received or pushed out. That means that it is also one of the most vulnerable areas of a network. The global endpoint security market is projected to reach almost US$ 13.4 billion in 2023. It should come as no surprise that endpoint detection and response platforms choose to integrate big data analytics to provide comprehensive protection. Endpoint Detection and Response platforms will scan all incoming and outgoing traffic for specific types of data, including:

• Processes
• Files
• Connections
• Users
• Systems

Sangfor’s Endpoint Secure platform will then leverage the collected data to ensure unified and comprehensive data across a now completely visible network. The platform also uses a combination of static and dynamic AI-based detection engines to defend the network against all types of ransomware – blocking them in just 3 seconds to ensure minimal damage. Big data is leveraged by Endpoint Secure in the form of ransomware indicators of compromise collected from over 12 million devices using the platform. This allows the solution to achieve a detection accuracy rate of 99.83%.

The Endpoint Secure solution focuses on 3 stages of a cyber-attack to employ comprehensive protection:

1. Pre-Attack – This phase focuses on prevention and makes use of endpoint discovery, unified endpoint management, vulnerability and patch management, and the configuration of baseline checks.
2. During an Attack – In this phase, passive detection and active protection are both central elements. Passive detection features used here include signature-based detection, behavior-based detection, AI and machine learning, and sandboxing. The active protection part during an attack includes micro-segmentation, ransomware honeypot, two-factor authentication, and brute-force attack detection.
3. Post-Attack – In the last phase after an attack, Endpoint Secure will look at residual threat detection and forensic analysis – which incorporate elements of threat hunting and threat correlation and visualization, respectively.

Sangfor’s Solution for Ransomware features released Endpoint Secure 6.0.2 and Network Secure firewall, recognizes that detecting and blocking is the best effort so it concentrates on threat hunting to find the APT that successfully infected your organization. Click here to learn how to Stop Ransomware in 3 Seconds in our Security Webinar by Sangfor Chief Product Officer.

Big data analytics also finds many uses in cyber security through the platforms for continuous threat detection. We now look at some of the ways Sangfor’s threat detection platforms have leveraged big data analytics to proactive hunt for cyber threats.

Big Data Analytics for Cyber Security Through Continuous Threat Detection

In a dynamic world of cyber threats that keep evolving and becoming more resistant, it’s crucial to invest in a Continuous Threat Detection platform for your cyber security. As threats adapt to newer security measures, your security measures need to adapt as well. Sangfor’s Continuous Threat Detection is one of the leading solutions when it comes to threat hunting, threat detection, and network traffic analysis.

Sangfor’s XDDR framework combines a holistic response to malware infections and APT breaches across networks with ease of management, operation, and maintenance. Sangfor is a strong believer in creating integrated threat-hunting solutions form a coordinated response to every data breach – as it just takes one breach to destabilize a network.

Sangfor’s Continuous Threat Detection and Cyber Guardian collaborate and correlate with big data analytics to provide a seamless proactive threat-hunting environment to secure your network from new and existing malware.

Cyber security big data allows Sangfor’s advanced and specialized platforms to perform real-time, efficient analysis to ensure better protection and elevated visibility for all users.

Choosing Big Data Analytics for Cyber Security

Big data analytics in cyber security is a growing field. As technology and cyber threats evolve, more platforms and services in the industry need to take advantage of the benefits of big data cyber security. Big data can be used to collect actionable insights that can drive practical cyber security solutions. As we’ve seen, big data in cyber security has been leveraged by network detection and response platforms, endpoint detection and response solutions, and continuous threat detection use cases.

Combining big data and cyber security ensures comprehensive solutions across industries. Sangfor is dedicated to expanding its leading cyber security range while leveraging the power of big data analytics across all platforms. Contact Sangfor today to see our wide range of cloud computing and cyber security platforms or visit www.sangfor.com to see how the future of big data analytics in cyber security is revolutionizing the industry.

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