Network Security and Anomaly Detection

AI can largely increase the detection accuracy of attacks and other threats flowing through the network and impacting end devices, without sacrificing the robustness of the analysis (i.e., false alarms). AI can also improve the automatic detection and diagnosis of the ever-growing number of anomalies occurring in all sort of complex systems, including data networks but not limited to that. By enabling the analysis of highly-dimensional data with both real-time and large-scale requirements, AI and big data principles and platforms can significantly improve the visibility and understanding of cybersecurity events and anomalies, empowering an efficient diagnosis process.

Network Monitoring

Network Traffic Monitoring and Analysis (NTMA) is a key component in network management and is needed to guarantee the correct operation of large-scale and/or complex networks. At AIT we design scalable online and offline data mining and machine learning-based techniques and platforms to monitor and characterize extremely large volumes of network traffic data and carry out NTMA.

Network Quality of Experience

Quality of Experience (QoE) is a well-known concept that permits operators, vendors, content providers, and distributors to understand and assess the functioning of networks and services from the standpoint of the end user or service customer. AIT’s AI-based Internet-QoE relies on big data analytics to generate useful user-centric insights from large-scale network measurements, even under the increasing prevalence of end-to-end encryption.