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Big Data Analytics: A Weapon Against Rising Cyber Security Attacks

In the past, sophisticated cyber criminals rarely attacked small and medium-sized businesses (SMB) due to their low brand recognition and lean financial resources. Today, SMBs rely on resources that do not provide robust security like cloud computing, servers, and APIs. As a result, attackers take advantage of this opportunity to steal sensitive data and cause damage

Traditional security tools are no longer effective against a multitude of complex cyber attacks. Organizations must leverage the potential of big data analytics to tackle the increasing amount of attacks.  

What Is Big Data Analytics?

Big data analytics apply advanced analytics and business intelligence tools to huge volumes of data, for the purpose of extracting insights. The analysis process involves elements like statistics, machine learning algorithms, and predictive models driven by high-performance computing systems. 

The processed information can be comprised of a mix of semi-structured and unstructured data. For example, mobile phone records, social media content, Internet of Things (IoT) sensor data and more. 

The purpose of big data analytics is to discover valuable information that can help organizations make informed decisions. For example, consumer preferences and market trends. This data can help companies improve their customer service, enhance operational efficiency, and find new business opportunities.

4 Tips for Preventing Security Incidents with Big Data Analytics

Data is a valuable asset for companies of any size. They need to collect and analyze large volumes of user data to stay competitive in the current market economy. As a result, privacy and security have become their main concerns. Companies are constantly looking for new ways to protect sensitive business data from hackers. This is where big data analytics comes in.

Companies analyze sophisticated cyber threats, like attacks targeting endpoints, with big data analytics and machine learning tools, to find any possible security threat that can put the business in danger. 

Big data security analytics tools can provide prioritized real-time threat alerts. These tools also provide additional forensic details for quick mitigation of cyber attacks. Below, you’ll find tips for leveraging big data analytics to prevent cyber security threats.    

  1. Predictive data analysis

The combination of machine learning and big data analytics creates a variety of new possibilities for cyber security. Big data analytics collects and analyzes data based on historical cyber attacks. Organizations can use machine learning and big data analytics to develop individual defense responses. In addition, you can respond to breaches in real time, find unique vulnerabilities in your network, and correlate information from different sources. 

  1. Monitoring and automation

Insider threats are responsible for many cases of security threats. One option to mitigate insider threats is to block access to sensitive information to unauthorized employees. As a result, authorized staff will need to use login credentials and other security measures to change data and view files. 

System administrators should supply login details based on the employee’s level of responsibilities. Big data analytics plays a crucial role in preventing and mitigating employee-related data breaches. Big data analytics tools monitor and analyze user behavior to detect any unusual activity; while automation enables security teams to respond to threats as they occur.  

Intrusion detection

The combination of big data analytics, network traffic, system events, and logs can help organizations to discover anomalies and malicious activities. 

Intrusion Detection Systems (IDS) monitor all the traffic in the enterprise network and identify any malicious activity or traffic anomalies. Big data analytics provides all the information required for company network monitoring, thus helping companies make informed decisions regarding intrusion detection system deployment.

Intelligent risk management

Cyber security mainly relies on actionable intelligence and risk management. Big data analytics tools improve cybersecurity efforts with intelligent risk management insights. Security experts can leverage these insights to interpret data more quickly and easily. As a result, organizations can handle security incidents without any delay.   

Big Data Security Analytics Tools

Big data analytics tools can analyze large volumes of data with different data types. Organizations that need to protect sensitive and important enterprise data should consider investing in the following big data security analytics tools:

  • IBM QRadar—a natural option for large enterprises that must maintain detailed event data. The platform uses a distributed SIEM system that provides horizontal data storage scaling. In addition, IBM QRadar uses minimizes management complexity and costs by using data nodes instead of storage area networks.
  • RSA Security Analytics—provides additional details about network sessions by analyzing network traffic. Security teams can leverage these insights to understand the wider picture of low-level security data.
  • LogRhythm—big data analytics solution that supports different data types like security events, system logs, audit logs, application logs, and flow data. The platform provides information about process activity, file integrity, network, and user behavior by analyzing raw data from these sources. 
  • Splunk Enterprise Security—allows security analysts to identify malicious events and collect data about the context of those events by using visual data correlations. The data is stored in a schema-less way and indexed on ingestion to enable varying data types while still providing rapid query response.

Conclusion

Big data analytics is critical to the success of businesses of any size. The combination of big data analytics tools with machine learning and AI, enables businesses to protect their network from cyber security breaches and attacks. In addition, big data systems enable security teams to automatically classify and categorize security threats. As a result, companies can eliminate many repetitive and manual tasks that cause long response delays.

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