Real-time and Continuously Monitoring of the HPC Cybersecurity using Bayesian Attack Graph
High-Performance Computing (HPC) security has the practical challenge to continuously monitor cybersecurity status. It is essential to develop software tools that enable continuous monitoring of the security status of the system in real-time so that the effectiveness of current security control can be evaluated. InfoBeyond advocates the R&D of HPC2M technology. Specifically, HPC2M is a scalable HPC Continuous Monitoring for Real-time Risk Assessment Using Distributed Bayesian Attack Graph to address the technical challenges of continuous monitoring of large-scale HPC networks for cybersecurity.
- By using Bayesian attack graph (BAG), the HPC2M system is designed as software to perform collection, aggregation, analysis, and presentation of security-related data of the large-scale HPC network for real-time risk assessment in a distributed manner. In other words, this tool enables automatic data collection and analysis to achieve real-time security risk assessments and effective evaluation of current security controls in a large-scale HPC system.
- Security risk metrics are calculated based on the Bayesian attack graph, then visualized to enable in-depth awareness of cybersecurity situation of the entire HPC system. This offers the capability to maintain the ongoing awareness of information security, vulnerabilities, and threats to support organizational risk management decisions in the HPC network.
HPC2M enables information security professionals and others to see a continuous stream of near real-time snapshots of the state of risk to their security, data, and network. In an HPC network, HPC2M can be deployed in a distributed way and can be easily scaled up to accommodate large-scale HPC network in a cost-effective way. By leveraging the automated data feeds and analysis, it reduces the manpower required for collection and analysis of security-related information for risk assessment.