Publications

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Decision-makers want to perform risk-based cost-benefit prioritization of security investments. However, strong nonlinearities in the most common physical security performance metric make it difficult to use for cost-benefit analysis. This paper extends the definition of risk for security applications and embodies this definition in a new but related security risk metric based on the degree of difficulty an adversary will encounter to successfully execute the most advantageous attack scenario. This metric is compatible with traditional cost-benefit optimization algorithms, and can lead to an objective risk-based cost-benefit method for security investment option prioritization. It also enables decision-makers to more effectively communicate the justification for their investment decisions with stakeholders and funding authorities. ©2010 IEEE.

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The Fiber Optic Intrusion Detection System (FOIDS)1 is a physical security sensor deployed on fence lines to detect climb or cut intrusions by adversaries. Calibration of detection sensitivity can be time consuming because, for example, the FiberSenSys FD-332 has 32 settings that can be adjusted independently to provide a balance between a high probability of detection and a low nuisance alarm rate. Therefore, an efficient method of calibrating the FOIDS in the field, other than by trial and error, was needed. This study was conducted to: x Identify the most significant settings for controlling detection x Develop a way of predicting detection sensitivity for given settings x Develop a set of optimal settings for validation The Design of Experiments (DoE) 2-4 methodology was used to generate small, planned test matrixes, which could be statistically analyzed to yield more information from the test data. Design of Experiments is a statistical methodology for quickly optimizing performance of systems with measurable input and output variables. DoE was used to design custom screening experiments based on 11 FOIDS settings believed to have the most affect on WKH types of fence perimeter intrusions were evaluated: simulated cut intrusions and actual climb intrusions. Two slightly different two-level randomized fractional factorial designed experiment matrixes consisting of 16 unique experiments were performed in the field for each type of intrusion. Three repetitions were conducted for every cut test; two repetitions were conducted for every climb test. Total number of cut tests analyzed was 51; the total number of climb tests was 38. This paper discusses the results and benefits of using Design of Experiments (DoE) to calibrate and optimize the settings for a FOIDS sensor

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JMP and design of experiments (DOE) have been successfully applied to security system technologies from sensors to communication and display systems. In all cases, the technologies have been complex enough to warrant the need for a statistical determination of significant factors and/or the generation of predictive models. For the sensors, it was the task of calibrating a fiber optic intrusion detection sensor (FOIDS) with 32 adjustable settings. In addition to the numerous settings, the FOIDS also had two software processors for detecting different types of alarms. The problem was made more complex when the different types of alarms occurred on the wrong processors, causing nuisance alarms. JMP's ability to optimize several predictive models simultaneously with JMP's Prediction Profiler flash files was an important factor in producing field solutions. For the Communications and Display testbed system, numerous hardware and software network components had been integrated to build a functional system. Although the components of the system had been tested individually, the system's performance could not be piecewise evaluated. Through the application of JMP's design of experiments and data mining capabilities, it was possible to test some of the factors affecting the system's performance and to differentiate between some of the software and hardware contributors. This paper will discuss design of experiments and the JMP tools applied to the solutions for both security systems.

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In manufacturing, the conceptual design and detailed design stages are typically regarded as sequential and distinct. Decisions made in conceptual design are often made with little information as to how they would affect detailed design or manufacturing process specification. Many possibilities and unknowns exist in conceptual design where ideas about product shape and functionality are changing rapidly. Few if any tools exist to aid in this difficult, amorphous stage in contrast to the many CAD and analysis tools for detailed design where much more is known about the final product. The Materials Process Design Environment (MPDE) is a collaborative problem solving environment (CPSE) that was developed so geographically dispersed designers in both the conceptual and detailed stage can work together and understand the impacts of their design decisions on functionality, cost and manufacturability.