Why Network Security Needs to Have Big Data Analytics?

Businesses go through massive renovations now and then to accommodate their growing needs. A traditional system renders many businesses incapable of taking the right decision at the right time. Because of this, many enterprises are now leaning towards the analytics to help them bring about an informed transformation. 

Introducing major changes in any enterprise setting makes analytics your best bet since it provides individuals with the right information concerning better customer experience, increased growth, improved ROI, and reduced overheads. All of this makes analytics indispensable to businesses around the globe. 

Corporate networks are no different. Since they produce and consume massive data on daily basis, it becomes necessary for them to rely on analytics to help them comprehend and protect the crucial information from prying eyes. 

Key Challenges to Combating Network Security Issues

Many businesses lack complete knowledge of all the assets they have in their inventory. This poses a massive problem because if such enterprises do not have the knowledge about their inventory, then how can they be sure that their networks are secure? 

Another challenge is that many enterprises own an open network structure. Once the attacker breaches the security network, they can have unrestricted access to all the major systems available on the network. With that being said, these network security issues are just the tip of the iceberg. The main challenges that hinder network security’s solutions from taking full-effect can be attributed to two things: data volume and scalability.

The traditional analytic tools are designed in a way that renders them useless when it comes to handling a large amount of data. On top of this, the SQL-based scalability tools are proving themselves incapable of processing a growing amount of information. 

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Big Data Analytics - Savior of All

Every now and then, the majority of companies face cyber attacks on their networks. In order to be able to detect them and tackle them early on, enterprises have started relying on big data analytics. 

Big data analytics work because it follows the PDR (Prevent, Detect, and Respond) paradigm. With that being said, many data analysts are urging companies to take on the big data analytics to prevent further security attacks from taking place. Also, big data proves useful in the following ways: 

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Real-Time Identification of Anomalies

Anomalies can happen in any form. According to a Harvard Business Review, 60% of the anomalies take place from the inside, meaning that many employees are responsible for data breaches. Sometimes, such data breaches result from a honest mistake where a certain employee sends sensitive information to the wrong individuals. However, many of such data leaks are intentional. Since such threats come from the inside, they are the hardest to detect but with the help of big data, everything can be detected on a timely basis. 

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Assessment of Network Risks and Vulnerabilities

Big data analytics comprehends a company’s data to identify and categorize it. Moreover, it identifies the risks and informs the individuals of all the network vulnerabilities that can be easily eliminated.  However, all of this information can be rendered useless if the enterprises fail to act upon these security issues on a timely basis. 

The biggest obstacle when it comes to the assessment of security risks and vulnerability does not lie in the inabilities of people to deal with this problem. It lies in their ignorance and their lack of urgency to deal with cyber risks in a timely manner. Because, no matter how much big data analytics help you in identifying holes in your network security, the data is useless unless you work on it to make the matters right. 

Improvement of Incident Response

When a data breach remains successful, many enterprises are challenged with the task of understanding how, why, and where the cyber attack took place. Often, companies rely on third parties to assess the damage and investigate the conditions that made the attack possible in the first place. All of these tasks can be easily managed with big data analytics. 

As mentioned earlier, big data analytics follows a PDR paradigm. Because of this, it has an improved incident response approach that allows it to address and assess the damage done by any security breaches or cyber attacks. 

Ideal Big Data Analytics Approach

Big data analytics continue to amaze enterprises by delivering intelligence that enables individuals to make faster and smarter choices. But, while people may rave about the capabilities of big data analytics, there are various trends, insights, and architectural tools that you need to sort through. Working your way through them will help you approach big data analytics successfully. 

Research and Analysis of Malware

Cyber attacks are becoming more sophisticated in their approach. They sneakily enter the system and go undetected until the damage is done. Big data analytics allow you to identify and report the threat. 

Trend Analysis in the Field of Cyber Security

Big data analytics gathers information regarding any cyber threat. It prepares a report that identifies a pattern, trend, or the path of the malware to predict future malware events. Thus, it saves enterprises from falling into the same trap again. 

Evaluation of Threat Detection Performance

Analyzing the trends, predicting the paths of the cyber threats, and identifying the malware is done in a bid to help the enterprises in making a right decision when it comes to network security. 

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Traditional analytics has been popular among the businesses for years; helping enterprises accrue data and working it in a way that allows them to benefit from it. People have always depended on traditional data analytics that aided them in securing threats in a professional manner. But since then, a lot has changed. 

Sophisticated malware combined with complex network breaches have made the traditional analytics redundant. On the other hand, big data analytics helps companies identify hidden risks. Moreover, it aids individuals by analyzing trends that give them an edge over their competitors. So, to understand big data analytics, you need to master the right programming language. You can hire PHP programmers who also seem to have an understanding of Python, SQL, and Java. 

Although big data analytics is extremely useful, do you think its expensive nature makes companies hesitant in integrating big data analytics in their network security system ? Share your thoughts with us in the comments section. 

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Last updated: 03 Apr 2023
About Author

Ravindra Savaram is a Technical Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.

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