Big Data Analytics Could Be a Powerful Weapon Against Cybersecurity

Undoubtedly, the technological landscape is introducing the latest innovations with each passing day. Although such innovations have transformed the way we do business, they have also opened several routes for cybercriminals. 

Consequently, the infrastructure of Information Technology faces several challenges in terms of data security. Today, around 23 million IoT devices are interconnected, which makes them vulnerable to cyber attackers. 

Why is Data Security Vital? 

Data, in any form, is vital for every organization. Whether we talk about customer and sales data, or financial and historical data, every part is imperative. This data serves as a base for planning future tactics. Therefore, it is essential to crafting a complete data security plan. Besides this, accurate testing of this plan will provide a safer protective layer to the company. 

How Can Big Data Analytics Help? 

Though various devices and techniques to combat cybercriminals are present, the growing severity of attacks seems to fail everything. However, big data analytics is proving to be a safer option. Several types of research have been done to know the relation between cyber-attacks and data analytics. One survey held by the U.S government revealed about 90% of firms admitting a decline in cyber-attacks. 

What Constitutes As Big Data Analytics? 

Big Data Analytics is a process to study and comprehend large sets of data. The aim is to understand the patterns and relations among several consumer-related things. Besides this, firms also extract valuable information from this procedure. 

The process involves various data science models and statistical techniques for detecting speed. Additionally, threat analysis is also conducted with real-time streaming. 

Thanks to the emerging technology that has helped organizations in using smart algorithms and process large chunks of data. These technological tools send automatic notifications as security alarms for securing business data. 

Additionally, other advanced tools also help in early detection and clearing all modifications related to cyber threats. During my research into big data analytics, I met a team of artificial intelligence developers. This team of experienced professionals explained how modern-day firms are using this technique. 

Let me share the key points in detail: 

1. Historical Data Analysis 

Several types of viruses and malicious activities result in a hefty expense for firms in terms of losing data. Therefore, an analysis of historical data is used to predict any malicious activity. For this, a standard model or a baseline is decided. 

Any deviation from this model helps in picking up an abnormal activity. This helps organizations in planning out counter activities to stay safe from threats. The procedure is also helpful in determining a quantitative vulnerability of the attack. 

Therefore, appropriate measures are planned accordingly. Additionally, any compromised data is sent directly to the professional team for re-evaluation and taking the right measures. 

Moreover, this data evaluating process plays a crucial role in designing artificially intelligent algorithms, statistical procedures, and future models. 

2. Workflow Management and Automation 

Big data analytics is also helpful in automating the workflow system. It aids in the management and monitoring of employee records. Some data breaches are also the result of employee involvement. Automatic monitoring is imperative for the resolution of such cases.

Additionally, many data hacking incidents happen in multiple firms. Internal employees are the key criminals in these hacking events.  Smartly managing the workflow activities helps in the early detection of threats and malicious activities. 

Moreover, data analytics further help by sending suspicious activities to the concerned professionals. It means that the records of millions and billions can be processed within minutes to detect the abnormality. 

3. Intrusion Detection Deployment 

Big data analytics can successfully pick up criminal activities by combining events, network flows, and logos. Though traditional data security techniques like multi-factor authentication, firewalls, and data encryption are effective, IDS is more powerful. 

The growing complexities of cyber-attacks have made it mandatory to use an Intrusion Detection System. Generally, there are three common categories of IDS. These include host-based, network-based, and hybrid. 

A host-based system detects cyber threats on a single PC while a network-based system analyzes the traffic. For this, sensors are kept at various network points. The third hybrid category analyzes the changes in the file system, application logs, and system calls. 

Additionally, there are advanced data techniques. Two of these include the stream processing and Hadoop ecosystem. These techniques can help in the following ways: 

  • Getting huge chunks of both internal and external data 
  • Deriving the given data and then analyzing it deeply 
  • Providing real-time examination 
  • Giving a thorough description of information concerning security

4. A New Functionality for Machine Learning 

Machine learning lets computers behave in a more advanced manner. Each time, a computer learns something from what has been fed in it in the past in comparison to human interaction. It means when we combine big data analytics with machine learning, it opens new doors of cybersecurity. 

With the help of gathering, combining, and analyzing previous cyber-attacks, machine learning can suggest valuable precautionary measures for the firm. Besides, the latest ML algorithms can identify the email address of the malicious email sender.  

Thus, modern-day firms are using the power of ML and combining it with data analytics for immense data security. 

5. Stream Data Processing 

This technique makes it possible to analyze data right after its availability. Therefore, it reduces the risk of huge data losses. By connecting with a few external sources, provides valuable insights and is helpful in cases where continuous dynamic data is generated. 

Wrapping It Up 

In short, cybercriminal activities are increasing with each passing day. Besides using the traditional tactics of cybersecurity, companies should look inside big data analytics. This technique is viable for the protection of valuable information. 

It makes use of historical data, automates the workflow process, and combines data analytics with machine learning. Besides this, it also uses stream data process technique for providing immense security. 

Are you also concerned about the growing number of cyber threats? Use the power of big data analytics and safeguard your company’s data in no time.  

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