A Comparison of Weighted Stochastic Simulation Methods

1ABSTRACTRare events are of particular interest in biology because rare biochemical events may be catastrophic to a biological system. To estimate the probability of rare events, several weighted stochastic simulation methods have been developed. Unfortunately, the robustness of these methods is questionable. Here, an analysis of weighted stochastic simulation methods is presented. The methods considered here fail to accomplish the task of rare event simulation, in general, suggesting that new methods are necessary to adequately study rare biological events.

Estimation of cyber network risk using rare event simulation

Inherent vulnerabilities in a cyber network’s constituent machine services can be exploited by malicious agents. As a result, the machines on any network are at risk. Security specialists seek to mitigate the risk of intrusion events through network reconfiguration and defense. When dealing with rare cyber events, high-quality risk estimates using standard simulation approaches may be unattainable, or have significant attached uncertainty, even with a large computational simulation budget. To address this issue, an efficient rare event simulation modeling and analysis technique, namely, importance sampling for cyber networks, is developed. The importance sampling method parametrically amplifies certain aspects of the network in order to cause a rare event to happen more frequently. Output collected under these amplified conditions is then scaled back into the context of the original network to provide meaningful statistical inferences. The importance sampling methodology is tailored to cyber network attacks and takes the attacker’s successes and failures as well as the attacker’s targeting choices into account. The methodology is shown to produce estimates of higher quality than standard simulation with greater computational efficiency.