Real-time Cyber Risk Assessment Using Dynamic Markov Models
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Solution Overview
Problem
Existing cyber risk assessment methods fail to dynamically determine risk in specific regions of a network and project it into the future, lacking the ability to accurately quantify vulnerabilities and exploit likelihoods in real-time due to uncertainties and incomplete measurements.
Innovation Solution
The use of integrated Bayesian, Markov, and state space models to encode quantitative and qualitative knowledge of cyber security vulnerabilities and exploits, creating dynamic representations of vulnerabilities and attack impacts, allowing for real-time risk assessment and defense against potential attacks by determining dependencies and exploit likelihoods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional risk assessment methods are used, then vulnerability identification is performed, but the ability to dynamically determine risk in specific network regions and project it into the future is lost
Solution Approach 1:
The patent implements dynamic risk assessment by continuously updating vulnerability states, exploit likelihoods, and attack impacts in real-time. The system transitions from static vulnerability databases to dynamic models that incorporate live network observations, enabling risk assessment that adapts to changing network conditions and emerging threats.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor network observations, sensor data, and attack indicators, using this information to update risk assessments. This feedback mechanism enables the system to learn from actual network behavior and improve its risk determination accuracy over time.
2Measurement precision
If comprehensive vulnerability scanning is performed, then vulnerability identification improves, but real-time risk quantification is hindered due to uncertainties and incomplete measurements
Solution Approach 1:
The system performs preliminary actions by pre-establishing vulnerability databases, attack graphs, and risk models before actual attacks occur. This preparation enables rapid real-time risk quantification when threats emerge, as the system can quickly match observed indicators against pre-built knowledge structures without needing to perform comprehensive scanning during incident response.
Solution Approach 2:
The patent introduces intermediary components including risk assessment engines, data fusion modules, and inference systems that mediate between raw sensor data and final risk determinations. These intermediaries process and contextualize observations, enabling real-time risk quantification by filtering and interpreting data through established security knowledge bases.
3Adaptability or versatility
If integrated Bayesian, Markov, and state space models are used, then dynamic risk representation improves, but system complexity increases
Solution Approach 1:
The patent segments the complex risk assessment system into distinct modular components: Bayesian networks for vulnerability modeling, Markov models for attack progression, and state space models for real-time estimation. Each module handles specific aspects of risk assessment independently, making the overall system more manageable while maintaining comprehensive dynamic representation capabilities.
Solution Approach 2:
The integrated models serve multiple functions simultaneously: they quantify vulnerability likelihoods, predict attack progression, assess real-time risk states, and generate defensive recommendations. This multi-functionality reduces the need for separate specialized systems, managing complexity while enhancing adaptive risk representation.
4Reliability
If real-time risk assessment is implemented, then defense capability improves, but computational resources and processing time are consumed
Solution Approach 1:
The system performs partial risk assessment actions focused on the most critical vulnerabilities and attack paths identified through preliminary analysis. By prioritizing assessment resources toward high-impact areas rather than uniformly scanning all possible vulnerabilities, the system achieves real-time defense capability with reduced computational resource consumption.
Solution Approach 2:
The patent dynamically adjusts assessment parameters and model complexity based on current network risk states. When risk levels are low, the system uses simplified models with fewer computational operations. When threats are detected, the system transitions to more comprehensive analysis modes, optimizing the balance between real-time response and computational resource usage.
Data Source
AI summary
Provided are processes of monitoring or modifying a network of electronically connected assets that dynamically builds relationships and dependencies among detected vulnerabilities in one or more of the assets and sensor measurements so that risk assessment can be achieved more accurately and in real-time. A process includes: identifying a plurality of vulnerabilities on a network of electronically interconnected devices representing one or more critical assets; determining dependencies between each vulnerability in the plurality of vulnerabilities; creating a hidden Markov model representing an attack state of each vulnerability of the plurality of vulnerabilities; determining the exploit likelihood of each of the attack states at a first time; determining the most probable sequences or paths of the attack states; and identifying dynamically the risk of one or more of the critical assets based on the sequences or paths of attack states.