Methodology of Expert-Agent Cognitive Modeling for Preventing Impact on Critical Information Infrastructure

This scientific article presents a comparative analysis of different threat prediction models used in security systems. Our expert-agent cognitive model has shown to be the most effective across a range of threat levels, with the highest performance compared to other models. The Circular Protection and Life Tree models also showed promising results but are less effective at higher threat levels. However, the Interval Confidence Interval model showed the worst performance in this comparative analysis. We have also identified the optimal input parameter values for our expert-agent cognitive model, which result in a 95% improvement in its prediction accuracy. Our model achieves the highest quality of prediction at Knowledge Base = 100, Expert Rating = 5, and Threats = 500. The performance of our model starts at around 60% accuracy with 50 threats and reaches a peak of 80% accuracy at 200 threats, gradually decreasing at higher threat levels. Our findings indicate that our proposed model is recommended for predicting and warning about potential threats in security systems. Further research can help optimize the parameters of our model for even more effective threat prediction and warning.