Michael E. Kuhl

Cyber attack modeling and simulation for network security analysis

Cyber security methods are continually being developed. To test these methods many organizations utilize both virtual and physical networks which can be costly and time consuming. As an alternative, in this paper, we present a simulation modeling approach to represent computer networks and intrusion detection systems (IDS) to efficiently simulate cyber attack scenarios. The outcome of the simulation model is a set of IDS alerts that can be used to test and evaluate cyber security systems. In particular, the simulation methodology is designed to test information fusion systems for cyber security that are under development.

Organ transplantation policy evaluation

This paper on the UNOS Liver Allocation Model (ULAM) describes the building of a simulation model that supports policy evaluation for a national medical problem. The modeling and simulation techniques used in building ULAM include: fitting donor and patient arrival processes having trend and cyclic rate components using non-homogeneous Poisson processes (NHPPs) having exponential rate functions which may include both a polynomial and some trigonometric components; fitting distributions to data on transition times between states of medical urgency; application of variance reduction techniques using common random-number streams and prior information; organizing data structures for efficient file searching and ranking capabilities; the use of bootstrapping techniques for attribute sampling; the building of submodels employing biostatistical procedures such as Kaplan-Meier and logistic regression; and the characterization of performance measures within a complex political, economic and social environment. ULAM provides a means for producing quantitative information to support the selection of a liver allocation policy.

The use of simulation to determine maximum capacity in the surgical suite operating room

Utilizing ambulatory care units at optimal levels has become increasingly important to hospitals from both service and business perspectives. With the inherent variation in hospitals due to unique procedures and patients, performing capacity analysis through analytical models is difficult without making simplifying assumptions. Many hospitals calculate efficiency by comparing total operating room minutes available to total operating minutes used. This metric both fails to account for the required non-value added tasks between surgeries and the delicate balance necessary between having patients ready for surgery when an operating room becomes available, which can result in increased waiting times, and maximizing patient satisfaction. We present a general methodology for determining the maximum capacity within a surgical suite through the use of a discrete-event simulation model. This research is based on an actual hospital concerned with doctor/resource acquisition decisions, patient satisfaction improvements, and increased productivity

Introduction to modeling and generating probabilistic input processes for simulation

Techniques are presented for modeling and generating the univariate and multivariate probabilistic input processes that drive many simulation experiments. Among univariate input models, emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bezier distribution family. Among bivariate and higher-dimensional input models, emphasis is given to computationally tractable extensions of univariate Johnson distributions. Also discussed are nonparametric techniques for modeling and simulating time-dependent arrival streams using nonhomogeneous Poisson processes

High level information fusion for tracking and projection of multistage cyber attacks

The use of computer networks has become a necessity for government, industry, and personal businesses. Protection and defense against cyber attacks on computer networks, however, are becoming inadequate as attackers become more sophisticated and as the networks and systems become more complex. Drawing analogies from other application domains, this paper introduces information fusion to provide situation awareness and threat prediction from massive volumes of sensed data. An in-depth discussion is provided to define fusion tasks for cyber defense. A novel cyber fusion system is proposed to address specifically the tracking and projection of multistage attacks. Critical assessments of the developed attack tracking and threat projection sub-components are provided with simulation results. This pioneering work elaborates the benefits, limitations, and future challenges of high level information fusion for cyber security.

A simulation study of new multi-objective composite dispatching rules, CONWIP, and push lot release in semiconductor fabrication

This paper evaluates dispatching rules and order release policies in two wafer fabrication facilities (thereafter referred to as ‘fab’) representing ASIC (application specific integrated circuit) and low-mix high-volume production. Order release policies were fixed-interval (push) release, and constant work-in-process (CONWIP) (pull) policy. Following rigorous fab modelling and statistical analysis, new composite dispatching rules were found to be robust for average and variance of flow time, as well as due-date adherence measures, in both production modes.

An Automated Multiresolution Procedure for Modeling Complex Arrival Processes

To automate the multiresolution procedure of Kuhl et al. for modeling and simulating arrival processes that may exhibit a long-term trend, nested periodic phenomena (such as daily and weekly cycles), or both types of effects, we formulate a statistical-estimation method that involves the following steps at each resolution level corresponding to a basic cycle: (a) transforming the cumulative relative frequency of arrivals within the cycle (for example, the percentage of all arrivals as a function of the time of day within the daily cycle) to obtain a statistical model with approximately normal, constant-variance responses; (b) fitting a specially formulated polynomial to the transformed responses; (c) performing a likelihood ratio test to determine the degree of the fitted polynomial; and (d) fitting to the original (untransformed) responses a polynomial of the same form as in (b) with the degree determined in (c). A comprehensive experimental performance evaluation involving 100 independent replications of eight selected test processes demonstrates the accuracy and flexibility of the automated multiresolution procedure.

A simulation study of dispatching rules and rework strategies in semiconductor manufacturing

Two major operational components of semiconductor fabs that effect fab productivity are dispatching rules and rework strategies. Although prior research has been conducted independently on these two issues, the hypothesis here is that the interrelationship between the dispatching rules and rework strategies has a significant effect on the productivity of the fab. Moreover, the goal is to determine which combination of widely-used dispatching rules and new and existing rework strategies results in the highest level of fab productivity. To test this hypothesis, a four-factor experiment is conducted to determine the effect of dispatching rules, rework strategies, fab types, and rework levels on key fab performance measures. Five dispatching rules are combined with three previously studied rework strategies and the first bottleneck strategy which is developed in this study. The treatment combinations are compared based on fab performance measures.

Univariate input models for stochastic simulation

Techniques are presented for modelling and then randomly sampling many of the continuous univariate probabilistic input processes that drive discrete-event simulation experiments. Emphasis is given to the generalized beta distribution family, the Johnson translation system of distributions, and the Bézier distribution family because of the flexibility of these families to model a wide range of distributional shapes that arise in practical applications. Methods are described for rapidly fitting these distributions to data or to subjective information (expert opinion) and for randomly sampling from the fitted distributions. Also discussed are applications ranging from pharmaceutical manufacturing and medical decision analysis to smart-materials research and health-care systems analysis.

Using simulation software to solve engineering economy problems

Some information required for solving problems in engineering economy problems can be fairly well defined. Much required information is uncertain, such as the actual cash flows from revenues and costs, the salvage value of equipment, the interest rate or even the project life. Engineering economy problems with all deterministic inputs are actually special cases. Probability descriptions of input variables and Monte Carlo sampling together provide a practical method of finding the distribution of the desired output given the various random and deterministic input variables. This paper provides three examples that demonstrate how commonly available simulation software could be used in engineering economy problems. One example is demonstrated that generates the distribution future worth of an annual series of payments when there is uncertainty about the future earning power (interest rate) from year to year. Also, an example is demonstrated that models the uncertainty of interest rates and the uncertainty of project life in order to generate the NPV distribution of a project. Finally, an example is presented to show the use of simulation in comparing alternative investment opportunities under uncertainty. These examples can be used to demonstrate how risk is handled in an engineering economy course. The examples can also be used as additional applications in an industrial simulation course.