Security
Supporting network security management is one of the main goals of our technologies. We have created an open-source framework for real-time analysis of the monitored flow data (NEMEA) and developed a number of methods for detection of various types of malicious network traffic. Many of these detection methods are based on modern machine learning techniques. We also undertake research and development in the area of Cyber Situational Awareness, focused on gathering data about sources of malicious activities (NERD) and on autodiscovery of...
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We are heavily using modern machine learning (ML) methods in the areas of network traffic analysis and security. We have successfully applied ML for classification of encrypted traffic, detection of different types of attacks, prediction of future attacks, or detection of coordinated behavior of malicious IP addresses. We are continuously exploring the potential of ML techniques in other use cases.
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