Stochastic Security Attack Impact Assessment

The patent transforms the assessment approach by changing the mathematical model parameters from deterministic to stochastic. It uses Continuous Time Markov Chains (CTMC) to model system states and transitions, enabling probabilistic assessment of security attack impacts. This parameter change allows the system to evaluate reliability under uncertain attack conditions rather than relying on fixed deterministic rankings.

The patent replaces the mechanical/Deterministic State Testing approach with a stochastic mathematical modeling system. Instead of using deterministic state transitions and fixed failure rankings, it implements a probabilistic model using Markov chains that can capture the random nature of security attacks and system responses, thereby improving measurement precision for stochastic threats.

The patent segments the system assessment into distinct components: resource usage modeling, failure modeling, and attack scenario analysis. By dividing the complex assessment task into separate Markov chain models for resource usage and failure patterns, it manages complexity while achieving accurate measurement of performance under various attack scenarios and resource configurations.

The patent introduces Markov chain models as intermediary mathematical structures between the physical system and the assessment metrics. These intermediary models translate complex system behaviors and attack patterns into measurable state transitions and probabilities, enabling accurate performance measurement without directly observing every system detail.