Next-Generation Analytics for Quick and Actionable Insights
Types of data processed & analyzed by NewEvol.
One of the biggest advantages of security analytics software is the sheer volume & diversity of information that is analyzed at a time like network traffic, endpoint & user behavior, cloud traffic, access & identity management data, etc.
This security analytics platform is a proactive approach to cybersecurity that is carried out with the help of machine learning analytics. ML which consists of Data Science, Data Mining, Algorithms & classical programming. It helps learn from experience & forecast probable outcomes.
Cybersecurity Analytics Features
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Descriptive
Based on data available at hand it tells what’s happening in your data. The factual data shows insights into how we can act and approach the future. These insights are in the form of data visualization like graphs, charts, reports, and dashboards.
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Diagnostic
The diagnostic features of security analytics help to understand the data faster and answer critical workforce queries. Interactive data analytics provides a root cases analysis of the incidents, anomalies and determines correlation or interrelation in data points.
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Predictive
The security analytics software represents the data that predicts realistic goals for the business using primary data. Based on historic data it spots core trends and patterns of the algorithms. To make future predictions, it considers data using machine learning algorithms and statistical patterns.
Security Analytics Benefits
NewEvol, the Best-of-breed Security Analytics Software, can identify patterns which reduces human efforts to a mere fraction of the time.
Prediction with Machine Learning
Machine learning algorithms gathered from all the security systems help to analyze real-time responses and predict the threat pattern of data breaches. This approach automatically correlates the collected threat data to find vulnerability patterns e.g.: Malware and Anomaly Detection, Phishing, Dos attack, etc.
Intrusion Detection in Real-Time
With Big data analytics, you can easily monitor and track the vulnerabilities in real-time with the help of advanced automation. Thus, it helps to block the threats before unauthorized access to the system is gained by an attacker.
Risk Management Reporting
Cybersecurity analytics is important to safeguard and keep your cyber defenses strong our security analytics solutions help you with risk management as well as reporting. It gives insight based on multiple sources to help with root cause analysis. For example, incidents like - Authentication, User Handling, Tasks during non-business hours, and much more.
Supervised Machine Learning
SL is the subcategory under Machine Learning & Artificial Intelligence. It uses labeled data sets to train algorithms and predict outcomes precisely. Multiple algorithms and computation techniques are executed to create precise machine learning models. The major role SL plays for the organizations is to categorize spam in a separate folder from your inbox.
Supervised machine learning can be used to develop or update business applications. SL can be segregated into two categories while data mining: Classification & Regression. Classification uses algorithms to assign data into specified categories. Common classification algorithms are logistic regression and decision trees while regression is used to make projections for sales and understand the relationship between predictable and unpredictable variables.
Unsupervised Machine Learning
UL is used to identify patterns in data sets for data points that are neither classified nor labeled data practices. It performs complex processing tasks and is often a generative learning model. From the unlabeled data sets, it uncovers patterns that help clustering without any training from previously known events. It is helpful when we are looking for anomalies that we are not aware of. It does not require base data with which the output can be compared to, hence it’s difficult to measure its accuracy.
UL is classified into two categories: Clustering and Association. Clustering helps to discover the intrinsic groups from the data while the association is used to suspect the rules that describe large portions of two or more data sets.
