Real-time Data Analysis
The Application Procedure follows below:
Data analysis is almost always useful, but real-time data analysis dramatically changes the ways systems can use data to predict outcomes and suggest alternatives. Instead of putting together conjectures based on a series of past events and recent scans, systems working in real time can deliver insights on what is going on right now.
For this reason, we developed a system that monitors network traffic for suspicious activity and issues alerts when such activity is discovered. The system passively records network traffic on the entire subnet for identifying particular patterns that are related with documented attacks. Once an attack is identified or abnormal behavior is observed, the alert can be sent to the administrator.
The incoming traffic is processed by a Machine Learning model and it is classified to known attacks based on the traffic’s patterns. Machine learning based methods have a better generalized property in comparison to signature-based IDS as these models can be trained and learn unknown patterns from the data.