How Is Big Data Analytics Used In Cybersecurity?

With billions of connected devices, the Internet of Things era has created an ever wider surface for cyber criminals to exploit, necessitating the need for fast and accurate detection of those attacks. In the last decade, advances in mobile computing, communications, and mass storage designs have resulted in the phenomenon of big data, which involves enormous amounts of useful data generated in diverse forms at a high rate. The capacity to analyze enormous amounts of data in real time with big data analytics tools provides numerous advantages that could be used in cyber threat analysis systems. Cyber threat analysts and intrusion detection/prevention systems can employ massive data acquired from networks, computers, sensors, and cloud systems.

 

Due to the need for fast processing of high-velocity, high-volume data from various sources to discover anomalies and/or attack patterns as quickly as possible to limit the vulnerability of the systems and increase their resilience, big data analytics will be a must-have component of any effective cyber security solution.

 

Despite the fact that many big data analytics tools have been developed in recent years, their use in the field of cyber security necessitates new approaches that take into account a variety of factors such as:

 

• unified data representation, 

• zero-day attack detection, 

• data sharing across threat detection systems, 

• real time analysis,

• sampling and dimensionality reduction

• resource-constrained data processing

• time series analysis for anomaly detection.

 

 

What is Cyber Security? 

 

Cybersecurity is the protection of internet-connected systems from cyberthreats such as hardware, software, and data. Individuals and businesses utilize the method to prevent illegal access to data centers and other digital systems.

 

A robust cybersecurity strategy can provide a good security posture against malicious attacks aimed at gaining access to, altering, deleting, destroying, or extorting an organization's or user's systems and sensitive data. Cybersecurity is also important in thwarting attacks that try to disable or impair the operation of a system or device.

 

The practice of defending computers, servers, mobile devices, electronic systems, networks, and data from hostile intrusions is known as cyber security. It is often referred to as information technology security or electronic data security. The word is used in a range of contexts, ranging from business to mobile computing, and can be classified into a few general categories. Network security is the technique of defending a computer network from intruders, whether they be targeted attackers or opportunistic malware.

 

Application security is concerned with keeping software and devices safe from threats. A hacked program may allow access to the data it is supposed to secure. Security starts at the design stage, long before a program or device is deployed. The processes and decisions for handling and securing data assets are included in operational security. This includes the rights that users have when connecting to a network as well as the protocols that govern how and where data can be kept or shared.

 

Disaster recovery and business continuity define how a company responds to a cyber-security incident or any other event that results in the loss of operations or data. Disaster recovery policies govern how an organization restores its operations and information in order to resume normal operations following a disaster. Business continuity is the plan that an organization uses when it is unable to operate due to a lack of resources.

 

Also Read | Ethics of Big Data: Balancing the Risks and Rewards

 

Big Data’s Role in Cyber-Threats

 

Risk management and actionable intelligence from big data analysis are required for cyber security. While data analysis tools are useful, the goal is to automate operations so that data is available more rapidly and analysis is delivered to the correct individuals on time. This will allow analysts to classify and categorize cyber threats without having to wait for data that may be irrelevant to the current attack. By taking the complexity of numerous data sources and condensing the patterns into visualizations, big data will also assist analysts in visualizing cyberattacks.

 

The ability to use data in its raw version allows disparate data to be useful not only with current events, but also with historical data. You can establish statistical baselines based on this historical data to determine what is "normal." You'll be able to tell when the data deviates from the typical. When indications are presented in real time, they might be easily overlooked; nevertheless, when seen over time, they can take on new importance.

 

Given the volume of data production and cyberattacks, it's not unexpected that Big Data appears to be the ideal solution for combating and preventing cybercrime. Cyber professionals can search through massive amounts of data in a timely manner to find and rectify issues, allowing them to even prevent and forecast future data invasion and data theft. Big Data analytics insights are critical for detecting and preventing cyber threats like ransomware, malware, phishing attacks, and so on. Using Big Data Analytics in cybersecurity entails analytical tools, data management, and inspection procedures that integrate data from many hosts, devices, and networks before delivering the information to sensors set therein to identify and prevent incoming threats. 

 

However, it is what you can do with this data, if anything, that will determine whether you are attacked or not. After all, data is only information until anything is done to improve cyber security. The ability to automatically respond to dangers detected in data, as well as having a high level of trust in the veracity of the data, is critical for a big data security solution.

 

How does Big data prevent Cyber Threat? 

 

Maintaining cybersecurity in an ever-changing threat landscape presents a challenge for all organizations. Traditional reactive tactics, in which resources were directed toward safeguarding systems against the most serious known risks while less serious dangers went undefended, are no longer adequate. A more proactive and flexible approach is required to stay up with shifting security dangers. Several important cybersecurity advisory bodies provide assistance. To guard against known and unexpected risks, the National Institute of Standards and Technology (NIST) suggests using continuous monitoring and real-time assessments as part of a risk assessment framework. Listed below are some ways through which big data can prevent cyber threat:

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How Does Big Data Prevent Cybersecurity

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1. Detection of Fraud:

 

Fraud is one of the most difficult concerns in cybersecurity. Millions of people use online services such as banks and social networks every day, and these businesses keep their personal information in order to identify them when they log in. If an attacker could gain access to this login procedure and hijack a user's account, they could do a lot of damage. Their actions would be untraceable, and it could take days or weeks for the person whose identity had been taken to notice that their information had been compromised.

 

2. Detection of Abnormalities:

 

Cybersecurity is always evolving; hackers create new strategies, and new tools are created to identify these new approaches. Understanding the behavior of any system is critical for this process, and it is also one of the primary strategies used in large-scale data analysis. Machine learning and anomaly detection approaches are used for some of the data analysis. This enables cybersecurity specialists to comprehend how any secure network or system operates and to detect odd behavior that should not be occurring.

 

3. Threat Prediction:

 

Companies can even identify forthcoming dangers using AI algorithms based on past and present records and the discovery of any patterns after examining the dataset. As a result, businesses can take the required precautions to avoid the threat from occurring.

 

4. Mobile Safety:

 

Big data is also being utilized to improve tablet and smartphone security. These devices are sophisticated computing tools used by millions of people all over the world, yet they can be appealing targets for cybercriminals because of the vast volumes of sensitive data stored on them by their users. Companies use big data to gain a thorough grasp of how client behavior shifts from desktop to mobile. This enables them to protect this critical data with the same cutting-edge security that they use on their commercial networks.

 

5. Situational Awareness:

 

Big data is also being used to give cybersecurity specialists with situational insight. This entails comprehending the complete environment and every activity occurring inside it, which entails investigating a wide range of aspects, such as whether a device is running regularly or if anomalous behavior is occurring. This allows for considerably faster mobile device security and provides network managers with data that aids in the prevention of data breaches.

 

6. Real-Time Intrusion Detection:

 

Companies can be alerted about any intrusions taking place, as and when they may occur, using data analyzing tools such as Apache Hadoop and HPCC, which can basically automate the entire process, allowing businesses or companies to stop it from happening.

 

Also Read | Five Tips for Ensuring the Best Cybersecurity Plan for Your Business

 

To Wrap Up:

 

Security experts may improve cybersecurity and minimize the amount of time they spend dealing with false alarms by leveraging big data to uncover activity trends throughout a business network. When something goes wrong, these tools can help determine the best course of action.

 

In some circumstances, it's safer to have a skilled cybersecurity professional on staff because distinguishing between normal traffic and something that could be an issue can be difficult. This problem is exacerbated by the fact that without effective technology, it can be difficult for human staff members to keep up with everything they have to perform each day.

 

A massive amount of data is acquired through numerous channels. Big Data necessitates cost-effective, time-efficient, and novel information processing methods for improved insight and decision making. When dealing with it, it is also vital to maintain a balanced approach to legislation and analytics. 

 

Processing large datasets becomes much easier with the help of a few data analysis techniques such as data mining and machine learning algorithms. To do this, potential tactics such as parallelization, divide-and-conquer, incremental learning, sampling, granular computing, feature or instance selection, and so on, can be used to reduce the computational time required to evaluate the data, resulting in more efficient outcomes.

 

Big data enables security specialists to evaluate more data in less time in order to make the best decisions possible, but they must still know when human involvement is required. Companies may build a more secure environment for their users and better protect their precious digital assets by combining the power of big data with the knowledge of cybersecurity professionals.