Radiflow and the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (Fraunhofer IOSB) have launched a joint research project for applying advanced machine learning and artificial intelligence to cybersecurity for industrial automation networks.
For this research project, Radiflow and Fraunhofer IOSB will collaborate on developing machine learning methods and artificial intelligence techniques for allowing the autonomous detection of non-compliant and anomalous behaviors on industrial automation networks. This applied research will involve evaluating graph-based and semantic approaches for event correlation and context awareness in order to develop these new machine learning and artificial intelligence capabilities.
The outcome of this research will be the development of a prototype for an Autonomous Industrial Cybersecurity Assistance System (AICAS) that expands on existing approaches for detecting deviations and anomalies to a baseline of network behaviors on OT networks. This prototype will be designed to self learn the underlying behaviors an of industrial automation networks and the functions of the connected assets in order to dynamically detect new and unknown cyberthreats.
“The question of how AI can enhance industrial cybersecurity to better respond to changing OT environments and new attack techniques is timely and essential,” said Dr.-Ing. Christian Haas, Group Manager at Fraunhofer IOSB. “Radiflow and its extensive experience working with industrial enterprises and critical infrastructure operators make the company the ideal research partner for applying AI to the industrial cybersecurity domain.”
The funding for this research project, which is scheduled to last two years, was granted by the Innovation Authority in Israel and the Federal Ministry of Education and Research in Germany.
At the conclusion of this research project, Radiflow intends to incorporate the new capabilities of this AICAS prototype into its iSID industrial threat detection system.
“Determining if abnormal behavior has been caused by normal operational activities or by cyber-attackers is critical for understanding and securing an OT network,” explained Yehonatan Kfir, CTO of Radiflow. “AI holds the potential to improve the situational awareness of OT networks by efficiently distinguishing between abnormal behavior that was caused by normal operations and abnormal behavior that is connected to a cyberattack.”
“We are excited to partner with Fraunhofer IOSB on this innovative research project,” added Kfir. “We expect that the outcome of this research will expand the cyber-monitoring capacities for our customers and MSSP partners with new capabilities that require less analyst input to highlight the most critical events on dynamically changing OT networks.”
The funding for this applied research project was coordinated by the Variance Ascola, a leading financial and economic advisory firm in Israel.
