What is Big Data?

Big data refers to extremely large volumes of information that are difficult to process with traditional analytical methods. Big data has five key elements: volume, velocity, variety, veracity, and value. The term was popularized by Gartner in 2011 and refers to data sets so large or complex that they cannot be analyzed using on-hand database management tools. Big data is critical for businesses because it contains valuable insights that can help a company gain an advantage over competitors. 

Big data analytics, also known as data mining, is a set of techniques and tools for extracting information from large datasets. It can be used to analyze everything from customer behavior to social trends. In this article we’ll examine how cyber security professionals are using big data analytics to identify and prevent attacks on their networks. IT Support Virginia can help your local business leverage data mining to better serve your customers and stand out from the competition.

Big Data and Cybersecurity

Cyber-attacks are on the rise and with the massive increase in the volume of data, it is becoming increasingly difficult to detect these attacks. Big Data can be used to monitor network traffic, identify insider threats and classify attacks. Big data can be used in cybersecurity operations to find common threats and reduce the time to detect and defeat attacks, monitor network traffic and detect irregularities, classify attacks and detect malware/ransomware attacks, observe file system data and find compromised or weak devices on the network, find insider threats.

Applications of Big Data Analytics in Cyber Security

Fraud detection with Big Data 

Big data analytics plays a tremendous role in fraud detection, with some techniques for statistical techniques in data analysis including pre-integrated techniques for error detection and validation. Fraudulent transactions can be identified through analyzing network traffic patterns; detecting changes in behavior patterns based on previous years’ transactions; anomaly detection based on user activity logs (e-commerce websites); detection pattern recognition using machine learning algorithms; identification of suspicious behaviors from customer service agents etc.

Algorithms that automatically detect anomalies

Big data analytics is a powerful tool for cyber defense engineers and data scientists to match algorithms for detecting anomalies in the behavior of transactions and users. Big data analytics not only strengthens cyber security networks through the above method but also helps eliminate false alarms, estimate potential risks, and predict future insights. 

One of its greatest advantages over human analysts is that they bring in a large amount of data, which enables them to be able to detect patterns and anomalies that humans cannot see otherwise. They also allow you to search through vast amounts of logs without having to manually sort through them all one by one, which can be very time consuming and tedious. Another benefit of big data algorithms is that they can process raw data much faster than humans do or any existing systems currently have the ability to do so. They are capable of analyzing billions of events per second (EPS) because they use parallel processing techniques where multiple processors work simultaneously on different parts of an algorithm at once. This means that processing time will decrease significantly. 

Big Data Analytics Can Enhance Security Intelligence

Traditionally, cyber defense management has been knowledge-intensive and time-consuming. However, the rapidly expanding depth of big data analytics has reduced the time needed to correlate data for forensics purposes and generate actionable security actions. Big Data Analytics can help analyse large sets of data to identify patterns that may indicate a potential threat. This includes identifying when someone is trying to access your system without permission or when an employee is accessing sensitive information. Big Data Analytics can also be used as part of artificial intelligence engines where they learn how humans behave in certain situations so they can be better protected against future attacks.

Big Data Analytics tools enable organizations to quickly analyse large amounts of data while delivering more accurate results than traditional methods such as manual reviews or keyword searches alone. For example, hackers often use specific techniques that are not easily identifiable by traditional security measures but these same techniques can be identified through machine learning algorithms which could indicate that a breach has occurred. 

More Efficient Calculation of Statistical Parameters

Big data analytics can be used to quickly calculate a variety of statistical parameters, including averages, quintiles, performance metrics, and probability distributions.

Better Clustering and Classification 

Clustering and classification are two important data analysis techniques that you can use to find patterns within the data. Clustering is the process of grouping similar items together, while classification is the process of assigning items into groups based on their features. These two techniques can be used together to discover anomalies in your data and identify potential fraud cases.

For example, if you want to identify fraudulent transactions on a financial account, then clustering will help you create clusters of potentially fraudulent transaction data from legitimate transaction records such as those relating to foreign payments or money laundering activities, which would then be identified by an algorithm based on certain parameters such as frequency or value ranges for each cluster. Cyber security professionals and data scientists can use big data analytics to easily cluster and classify patterns and associations among large groups of data. To know more about how big data can help businesses protect themselves from cyberattacks, please refer to Managed IT Services Virginia.

Post courtesy: Strategic Solutions, IT Support Provider in Virginia.