Automated risk assessment: a hierarchical temporal memory approach
Ricardo J. Rodríguez, James A. Cannady · 2010
Risk assessment models attempt to predict the probability of threats on systems in order to deploy countermeasures that will ensure system security and reliability. In recent years, risk models have become dynamic in nature, which resulted in a significant improvement over their static counterpart by taking into consideration that risk and its components vary over time. However, the evident complexity of the models and the rigorous mathematical approaches suggest significant domain constraints and lack of true human-like reasoning. This lack of higher cognitive skills in automated risk assessments stems from the gap that exists between neuroscience and artificial intelligence (AI). This paper discusses the potential of using hierarchical temporal memory models for improving human-like reasoning in automated risk assessments.