Data Analytics in Cyber Security Era
Data Analytics in Cyber Security Era
Thanks to the enormous growth in data over time, business organizations now have unmatched opportunities. However, the availability of Big Data across numerous storage repositories and the interconnected IoT system has raised the risk of cyberattacks and other security risks. Cybercriminals need one weak point in the system to get access and severely harm the targeted company's operations.

I will explain cyber security analytics in this blog and look at the various applications of data analytics in cyber security and the value of predictive analytics in cyber security.


Cybersecurity analytics: what are they?


Cyber Security Analytics is a sophisticated approach to cyber security that uses data collection, aggregation, attribution, and analysis methods to derive insightful conclusions. These insights, in turn, assist in carrying out vital security tasks that aid in identifying and thwarting online threats and attacks. Solutions for big data analytics in cyber security aid in collecting information from various sources, including business applications, virus scanners, operating system event logs, behavioral data, and more. Businesses attempt to combine this data into a unified data set that security professionals can use to apply the proper searches and algorithms (data analytics) for the early detection of cyber attack indications.


Data analytics in cyber security enables security analytics professionals to detect potential threats early on, giving them a chance to counter them before they can infiltrate the network infrastructure and cause data loss, breach, or leakage. Cyber Security Analytics includes a significant amount of machine learning technology. Real-time threat and data analytics are made possible thanks to it. Check out the trending data analytics course online to learn more about big data tools. 


The Mighty Union of Data Analytics and Cybersecurity:

Big Data, Security Analytics, or Cyber Security Analytics There are various names for the same phenomenon known as "cyber security analytics," which is the result of the union of "cyber security" and "data analytics." Data analytics is concerned with data management, including data collection and storage, as well as the methods, procedures, and tools that facilitate data analysis. The major goal is to derive important insights that could aid in creating predictions, carrying out business decisions and increasing efficiency. With the aim of defending them from hostile digital attacks, cyber security deals with providing safety and security to servers, computers, mobile devices, programs, systems, and networks.


According to the definition given above, cyber security analytics is the process of using data analytics to achieve a cyber security goal. You engage in Big Data Cyber Security Analytics when you use data analytics to understand data in a way that can identify vulnerabilities, describe risks, predict potential malicious attacks, and propose preventative measures.


The volume of data is continuously increasing exponentially. Therefore, searching through and investigating this data is vital to find any potential hazards. However, without a strong Big Data Analytics framework that is designed to work with the Cyber Security system, this is not possible. In order to create a strong wall of safety, Cyber Security Analytics work to create a synergy between Cyber Security and Data Analytics.


Need for Cybersecurity Analytics:


In this section, we'll take a closer look at a few of the elements that contributed to the development of big data cyber security analytics.


  • According to BARC and KuppingerCole's "Big Data and Information Security" survey, only 6% of businesses assert that they are impervious to cyber security risks, and 62% assert that threats have increased over the past 12 months.

  • Data can be structured using cyber security analytics to create a unified view of security breaches and threats, enabling better planning and quicker resolution.

  • One of the main forces for developing a Data Analytics Cyber Security mechanism has been the unprecedented increase in the frequency and sophistication of Cyber attacks. The threat itself can go unnoticed for abnormally long periods, even though it only takes one vulnerability for an attacker to knock the system down. Together, a strategic approach to cyber security and data analytics helps keep track of threat patterns and, as a result, issues alerts in the event of any anomaly. ‍


Cybersecurity Data Analytics Strategy:


Attempting to integrate data analytics into a cybersecurity framework necessitates creating a proactive plan. This course of action could go as follows:


  • Data Collection:-

Compiling all pertinent data from an organization's network into a single dataset is crucial. The information can be kept in cloud-based repositories and other places where cybercriminals find it difficult to obtain.


  • Sorting and Classifying Data:-

By using the traditional security taxonomy, you will need to normalize the data. For fields with common values that have common names by default, grouping can be done. This facilitates the streamlined use of search tools.


  • Additional Data Acquisition:-

It's crucial to increase the amount of data you have at your disposal. In order to unleash new capabilities, as much data as possible should be collected. It can enable the use of rigorous detection methods and aid in extracting contextual knowledge.


  • Boost your Security Information:

In this step, you can supplement and enhance your security data with information from both internal and external sources, including website data, business tools, and open-source feeds.


  • Automating the Cyber Security Analytics process:

Automation plays a major role in achieving the objectives of data analytics and cyber security. This is due to the necessity of real-time insight generation. Automation ensures timely data extraction and analysis of the resulting insights, and it can be strategically employed to issue an appropriate response in the event of a cyber threat.

  • Recognize and Catch:

Big Data Cyber Security Analytics is currently in the action stage. It entails the use of various threat detection methods and procedures, as well as the honed use of inquiries and research. Additionally, it's critical to choose the best type of detecting approach. A statistical approach, for example, will be better suited to observe circumstances where there has been an improbable spike in network traffic.


Tools for Big Data Analysis Analytics for cyber security:\


Various cutting-edge Big Data Analytics Cyber Security Tools have emerged due to the growing requirement to put in place a strong Cyber Security Analytics framework. This section will examine a few of these Security Analytics with the best data analytics which can be learnt in the best data analytics course.


  • Orchestration, automation, and response for security (SOAR):


This cyber security analytics system is a nodal tool that aims to connect the applications for threat response, data collection, and analysis.


  • Analytics for Behavior:


This approach looks to employ predictive analytics in cyber security by looking at and evaluating user and device behavior. In order to create generalizations and then find anomalies, these patterns are investigated. For instance, credit card theft is frequently detected with this approach. High withdrawal rates not common may be a sign of fraud.


  • Forensics:


Legal Big Data Tools for cyber security analytics make investigating current or prior threats easier. They also assist in identifying the methods in which the system's faults were used by cybercriminals and look for new vulnerabilities that could later hurt the firm.


  • A platform for Security Information and Event Management (SIEM):


The SIEM platform collaborates with various Cyber Security Analytics tools to gather security data across the network infrastructure and help with analysis and provisioning alerts on the discovery of potential threats.


  • Network Visibility and Analysis (NAV):


This tool is concerned with monitoring traffic as it moves throughout the network, as the name implies. It deals with network forensics, network discovery, flow data analysis, and network metadata analysis.


  • Software for Threat Intelligence:


These Cyber Security Analytics solutions aid in supplying important details regarding the most recent advancements in the Cyber Security industry. These include details about brand-new malware and other odd activities, including zero-day assaults. This makes security experts more ready for any upcoming threat or attack.


Use Cases for Cybersecurity Analytics:


The quest of maintaining a strong cyber security system has important benefits for both cyber security and data analytics. This section will examine a few Big Data Cyber Security Analytics application cases.


  • Analyzing Network Traffic:


You may get a glimpse of your network traffic thanks to data analytics in cyber security, which allows you to spot any form of network anomaly. You can also employ cloud security technologies to detect risks in the cloud environment simultaneously.


  • Finding Insider Threats:


A malicious insider accessing sensitive data through network credentials may also intentionally breach or leak data. Data analytics cyber security tools can detect insider threats by monitoring activities like unusual email usage, unauthorized database requests, unusual login times, and so forth.


  • Unauthorized Access to Data:


Data theft or data loss might lead to unapproved data exfiltration. Solutions for cyber security analytics can aid in blocking unauthorized communication channels and preventing people from providing their credentials to blocked websites.


  • Watching user behavior can help you spot threats:


Analytics of Data User and entity behavior analytics (UEBA) are used in cyber security to create algorithms that can identify patterns of malicious conduct in user behavior.



It is clear that Cyber Security Analytics has aided in strengthening the security system. The effective marriage of data analytics and cyber security has enabled the use of both fields' strengths. Data analysis enables early threat detection and improved preparedness and assists security analysts in developing sophisticated reaction and mitigation plans. At Learnbay, we give you a thrilling chance to develop skills that are in line with those of a Cyber Security specialist. Learn more about our data science course with placement on cyber security here.