Publications

Assessing the risk of malevolent attacks against large-scale critical infrastructures requires modifications to existing methodologies that separately consider physical security and cyber security. This research has developed a risk assessment methodology that explicitly accounts for both physical and cyber security, while preserving the traditional security paradigm of detect, delay, and respond. This methodology also accounts for the condition that a facility may be able to recover from or mitigate the impact of a successful attack before serious consequences occur. The methodology uses evidence-based techniques (which are a generalization of probability theory) to evaluate the security posture of the cyber protection systems. Cyber threats are compared against cyber security posture using a category-based approach nested within a path-based analysis to determine the most vulnerable cyber attack path. The methodology summarizes the impact of a blended cyber/physical adversary attack in a conditional risk estimate where the consequence term is scaled by a ''willingness to pay'' avoidance approach.

Abstract not provided.

With the increasing reliance on cyber technology to operate and control physical security system components, there is a need for methods to assess and model the interactions between the cyber system and the physical security system to understand the effects of cyber technology on overall security system effectiveness. This paper evaluates two methodologies for their applicability to the combined cyber and physical security problem. The comparison metrics include probabilities of detection (P{sub D}), interruption (P{sub I}), and neutralization (P{sub N}), which contribute to calculating the probability of system effectiveness (P{sub E}), the probability that the system can thwart an adversary attack. P{sub E} is well understood in practical applications of physical security but when the cyber security component is added, system behavior becomes more complex and difficult to model. This paper examines two approaches (Bounding Analysis Approach (BAA) and Expected Value Approach (EVA)) to determine their applicability to the combined physical and cyber security issue. These methods were assessed for a variety of security system characteristics to determine whether reasonable security decisions could be made based on their results. The assessments provided insight on an adversary's behavior depending on what part of the physical security system is cyber-controlled. Analysis showed that the BAA is more suited to facility analyses than the EVA because it has the ability to identify and model an adversary's most desirable attack path.

Assessing the risk of malevolent attacks against large-scale critical infrastructures requires modifications to existing methodologies. Existing risk assessment methodologies consider physical security and cyber security separately. As such, they do not accurately model attacks that involve defeating both physical protection and cyber protection elements (e.g., hackers turning off alarm systems prior to forced entry). This paper presents a risk assessment methodology that accounts for both physical and cyber security. It also preserves the traditional security paradigm of detect, delay and respond, while accounting for the possibility that a facility may be able to recover from or mitigate the results of a successful attack before serious consequences occur. The methodology provides a means for ranking those assets most at risk from malevolent attacks. Because the methodology is automated the analyst can also play 'what if with mitigation measures to gain a better understanding of how to best expend resources towards securing the facilities. It is simple enough to be applied to large infrastructure facilities without developing highly complicated models. Finally, it is applicable to facilities with extensive security as well as those that are less well-protected.

This document is a reference guide for the UNIX Library/Standalone version of the Latin Hypercube Sampling Software. This software has been developed to generate Latin hypercube multivariate samples. This version runs on Linux or UNIX platforms. This manual covers the use of the LHS code in a UNIX environment, run either as a standalone program or as a callable library. The underlying code in the UNIX Library/Standalone version of LHS is almost identical to the updated Windows version of LHS released in 1998 (SAND98-0210). However, some modifications were made to customize it for a UNIX environment and as a library that is called from the DAKOTA environment. This manual covers the use of the LHS code as a library and in the standalone mode under UNIX.

This article describes how features of event tree analysis and Monte Carlo–based discrete event simulation can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology, with some of the best features of each. The resultant object-based event scenario tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible. Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST methodology is then applied to an aviation safety problem that considers mechanisms by which an aircraft might become involved in a runway incursion incident. The resulting OBEST model demonstrates how a close link between human reliability analysis and probabilistic risk assessment methods can provide important insights into aviation safety phenomenology. © 2004, SAGE Publications. All rights reserved.

Event tree analysis and Monte Carlo-based discrete event simulation have been used in risk assessment studies for many years. This report details how features of these two methods can be combined with concepts from object-oriented analysis to develop a new risk assessment methodology with some of the best features of each. The resultant Object-Based Event Scenarios Tree (OBEST) methodology enables an analyst to rapidly construct realistic models for scenarios for which an a priori discovery of event ordering is either cumbersome or impossible (especially those that exhibit inconsistent or variable event ordering, which are difficult to represent in an event tree analysis). Each scenario produced by OBEST is automatically associated with a likelihood estimate because probabilistic branching is integral to the object model definition. The OBEST method uses a recursive algorithm to solve the object model and identify all possible scenarios and their associated probabilities. Since scenario likelihoods are developed directly by the solution algorithm, they need not be computed by statistical inference based on Monte Carlo observations (as required by some discrete event simulation methods). Thus, OBEST is not only much more computationally efficient than these simulation methods, but it also discovers scenarios that have extremely low probabilities as a natural analytical result--scenarios that would likely be missed by a Monte Carlo-based method. This report documents the OBEST methodology, the demonstration software that implements it, and provides example OBEST models for several different application domains, including interactions among failing interdependent infrastructure systems, circuit analysis for fire risk evaluation in nuclear power plants, and aviation safety studies.

Uncertainty distributions for specific parameters of the Cassini General Purpose Heat Source Radioisotope Thermoelectric Generator (GPHS-RTG) Final Safety Analysis Report consequence risk analysis were revised and updated. The revisions and updates were done for all consequence parameters for which relevant information exists from the joint project on Probabilistic Accident Consequence Uncertainty Analysis by the United States Nuclear Regulatory Commission and the Commission of European Communities.

Abstract not provided.