Secure Asset Inventory via Intelligent Scanning

Supply Chain Risk Management (‘SCRM’) of operational technology (‘OT’) networks is particularly challenging for software components. Software is continually updated to maintain and enhance its performance and cybersecurity capabilities. This requirement applies to almost all significant energy delivery system assets, as any network connected device inherently presents software cybersecurity risks. The foundation of reliable software SCRM begins with an accurate asset inventory. Current state-of-the art approaches to asset inventory are based on traditional IT systems and include: manual, active, and passive methodologies. Unfortunately, these single point solutions are not adequate to meet the needs of a rapidly evolving electric grid, nor the exponential rise in cyber-attacks on these OT based systems. To address these limitations, Operant is proposing to develop a next generation Intelligent Asset Inventory solution leveraging existing DOE funded cybersecurity tools and combining them with modern technologies: Artificial Intelligence and Named Data Networking, into a lightweight and cost-effective solution for industry. The research objectives of this Phase I project are to: 1) verify the ability to extend existing tools into an accurate OT asset inventory solution through a two-pronged intelligent approach comprising passive network scanning and periodic active probes;and 2) utilize deep learning (‘DL’) behavioral analytics to integrate the large amounts of data into human reviewable form, and provide feedback to further improve the system capabilities.