The volume and variety of data that is generated every second in the telecom industry grows exponentially. This results in big opportunities for every industry, as companies are seeking to have their data analyzed and the transformation of this information into valuable insights that will create a competitive advantage and help the evolution of their business.
All of this data needs to be processed and analyzed for security purposes. Years ago, "Deep Packet Inspection" was used to examine the contents of packets passing through an interconnection, but the use of new encrypted protocols for user’s privacy has made this technique obsolete, creating new challenges for network administrators, for whom it is essential to have access to the packets in order to look for malicious traffic and viruses, whilst not compromise the users privacy and the encryption of the packets. To this effect, SONOC has been working on new functionalities based on network anomaly detection tools and statistical techniques built using machine learning algorithms to detect suspicious patterns. We have also developed an alarm system to notify of suspicious activity based on automatic detection using KPIs.
Our technique allows us to inspect the traffic at the endpoints and detect anomalies in network traffic metadata, learning where each packet comes from and where it is supposed to go to, and thus inspecting encrypted traffic without decrypting it.
Features of our solution:
Powerful dashboard for optimized data-driven network security.
Surveys, analysis and operations linked to security.
Market supervision and detection of anomalies.
Measure real-time data traffic consumption on different networks.
Get detailed reports and up-to-date statistics on the users.
Determine the correlations between the different key indicators.