Network Anomaly Detection Software

It is very important to secure the network when the enterprise infrastructures are comprehensive and complex. Network anomalies, attacks, malware, or security threats can alter network security landscapes.

Motadata ObserveOps offers a comprehensive network monitoring solution that lets network admin monitor and analyze network activity with in-depth insights that provide complete visibility across network architecture.

Network security attacks can occur passively. Events when the attackers access, monitor, and steal crucial and sensitive information.

The active attacks would not only get access to the network data but also encrypt, modify and delete permanently.

These events can be in the form of attacks, malware, vulnerability exploits, persistent threats, etc.

Traditional vs. Anomaly Alerts

The traditional network monitoring system used to have a static approach where the admin gets to configure a static threshold to the critical metrics.

Then, the system notifies the network admin whenever the thresholds are breached or crossed.

These traditional approaches only help with the critical alerts but do not help you detect unusual behavior and track the patterns

With the help of AI-ML abilities on board, Motadata ObserveOps helps you detect anomalies.

It identifies the unusual behavior from the network operations and network device metrics based on historical audit data and alerts the network admin with its intelligence.

It is a continuous data model with behavior monitoring that applies Baselining to the metrics.

Configuring Anomaly Detection

Motadata offers two models to start analyzing patterns in your metrics behavior: Linear and Agile.

Based on your enterprise infrastructure and requirements, you can choose a model and start identifying the patterns to detect the anomaly and save the network enterprise from hazardous damages.

While setting up anomaly detection for any network enterprise, it is essential to identify the use cases and the type of network operations the infrastructure carries out.

Then, from the historical behavior, the monitoring system forecasts the possible behavior for that particular metric or process.

The forecasted values are compared with the real-time data.

Based on configured acceptable deviation value, real-time data will be compared, and an event/alert will be triggered if it violets accepted deviation range.

Better Network Dynamics with Anomaly Detection

Motadata ObserveOps uses different methods to determine when anomalous behavior alerts an issue notification.

The automatic multidimensional Baselining detects the violations of individual metric values that change over time, such as response times, error rates, etc.

It proactively detects the application traffic and CPU utilization abnormalities as they drive the network operations.

Motadata learns the application traffic patterns and raises an error notification whenever a statistical and relevant deviation is detected.

It helps the server better user experience and finds the possible cause behind the interruptions.

Monitoring an enterprise network has become a compulsion and essential for any business.

However, traditional monitoring tools are not enough to meet the market challenges and identify complex behavior patterns with the naked eye.

In addition, they are not enough to represent the enterprise’s metrics in a way that can make network performance efficient enough.

Motadata ObserveOps is an advanced monitoring tool that challenges traditional monitoring approaches and provides AI-ML-enabled facilities.

It helps network enhance their performance by identifying network anomalies and saves them from malware attacks and security breaches.