21. neutralization analysis - sandia.gov · 21. neutralization analysis abstract. response, along...

The Twenty-Sixth International Training Course 21-1

21. Neutralization Analysis

Abstract. Response, along with detection and delay, is one of the three major physical protection functions in the DEPO. Probability of neutralization (PN) is one of the measures of effectiveness of the response function, along with the comparison of delay times and response times. This probability determination first requires making the choice of a determination methodology and then requires information about the response forces, the threat, and the physical protection system (PPS). Information required includes not only specific characteristics such as weapons and training, but also the rules of engagement at the facility and the order in which response forces arrive. There are four general categories of methodologies to measure PN: expert opinion, mathematical models, simulations, and actual engagements.

21.1 Introduction

Probability of Neutralization (PN) Is the Measure of Effectiveness of

Response

The physical protection system (PPS) at a nuclear facility consists of detection, delay, and response functions. The purpose of the response function is to render the adversary incapable of completing his goal. The response function at a facility can be characterized by collecting the appropriate data. However, the analyst must still develop some measure of effectiveness of the response.

For sensors, the measure of effectiveness is the probability of detection. For barriers, the measure of effectiveness is the delay time. For response, the measure is probability of neutralization.

The determination of this probability will require information about the response forces, the threat, and the PPS, as well as the choice of a methodology. The purpose of this lecture is to provide the necessary information and a suggested approach to allow the determination of probability of neutralization.

Section Outline This course provides information regarding the following probability of neutralization topics:

Definitions and concepts related to probability of neutralization (PN) and its use in system effectiveness evaluation

Factors that Affect PN Methodologies used to determine PN Data and other factors required to estimate PN A simple numerical model to estimate PN

21.2 Definitions and Concepts Related to PN

Engagements and Wins

Before attempting to determine the effectiveness of a response force in neutralizing an adversary force, some terms must be defined. An engagement is defined as an event where two opposing forces, such as the response force and an adversary force, use weapons and tactics in an attempt to achieve their respective goals. Obviously, because many random variables are involved in the engagement, there are many possible outcomes. A win is defined as one of the following outcomes of the

Define Physical Protection System Requirements

21-2 The Twenty-Sixth International Training Course

engagement: the adversary force is killed, captured, or abandons the attack and flees.

Probability Probability is the chance that a given event will have a certain outcome. More precisely, if there exists a number n of equally likely possible outcomes to an event, of which a number s of these outcomes are regarded as favorable, then the probability of a favorable outcome is given by the ratio s/n (Reference 1). If the event under consideration is an engagement, then the favorable outcome is a win.

Probability of Neutralization

Equation

In light of the above, probability of neutralization is now easily defined by the following equation:

PN = N(wins) / N(engagements) The number of engagements in the denominator is a statistically significant number in accordance with the Law of Large Numbers. This law states that as the number of times in which an event is repeated becomes larger and larger, the proportion of successful outcomes will tend to come closer and closer to the actual probability of success. In using the defining equation in an analysis process, the analyst should kept in mind that all engagements must have the same initial conditions, and there are only two possible outcomes per engagement: win or loss.

Probability of Neutralization

Concepts

PN is a component of the overall effectiveness of a PPS, and measures the effectiveness of response after interruption. After interruption, the response force must use the force necessary to prevent the adversaries from completing their objective, which may require an armed engagement between the two sides. PN represents the likelihood of outcomes of engagements between adversary and response forces, but cannot predict the outcome of a single attack against a site. There are a number of methods used to measure PN, and they require acquiring different types of data for use in an analysis. Simple methods may only require data regarding the number of personnel and weapon types on either side, along with the time at which different numbers of each side are in an engagement. More complex models, such as simulations, may require a significant amount of data. Some examples include:

Initial locations of response forces and adversaries Response force deployment routes and final locations Adversary path Adversary scenario Terrain Building schematics PPS characteristics (e.g., barrier delays)

21. Neutralization


21.3 Some Factors Affecting PN

Factors affecting PN Since World War II, there has been heavy use of scientific methods for the study of military problems. Mathematical methods have been widely used for analysis of tactical engagements between opposing forces, and an armed attack on a nuclear facility has many similarities to an attack on a defended military site. Three mathematical based methods have been used: war games, simulations, and analytic models. In analytic models, mathematical algorithms and formulas are used to evaluate specific factors in an engagement. Each method has advantages and disadvantages, and no model can account for the significant number of factors that lead to the outcome of a specific engagement. There are many factors that affect the outcome of an armed engagement (Table 21-1). The effect of some on the outcome of an engagement can be analyzed qualitatively, some factors can be analyzed qualitatively, and some factors are very difficult or impossible to analyze. The following table shows some of the factors that affect PN.

Table 21-1. Some of the Factors that Affect PN Number of combatants Armored vehicles Weapon Type and Range Situational awareness Force multipliers (e.g., explosives, vehicle bombs, indirect fire weapons)

Tactical decision-making

Tactics Diversions Training levels Ambush tactics Deployment Terrain Reinforcements Morale Concentration of forces Surprise Multi-pronged attacks Environmental Factors Use of insider(s) Facility configuration Target Characteristics (transport, in process, in storage)

Command, control, and communications

Intelligence Non-combat attrition (accidents, sickness, desertion, etc.)

Use of hardened fighting positions

21.4 PN Analysis Methods

Methods for Determining PN

Methods for determining probability of neutralization (PN) include:

Expert judgment Mathematical models Simulations Analyzing the results of actual engagements

Each method has its advantages and disadvantages, primarily in terms of time, cost, and accuracy. Some methods can analyze a few factors, while some can analyze many more. No method is able to account for all the



factors that affect the outcome of a single engagement, but can provide insight into the strength of a response force. All methods are able to perform simple analyses from a small set of quantifiable data, such as the number of combatants on either side, the types of weapons used, and the response force time. Combined with subject matter experts to provide insights into data that can be qualitatively analyzed, these simple methods can be combined with expert judgment to account for other factors, such as the estimated number of response force casualties from a vehicle bomb detonated at an entry control point. The reduced response force numbers can then be used in the selected PN method. Some factors are difficult to analyze with any method, such as tactical decision-making, morale, and casualties from non-combat events such as response force members involved in a vehicle accident while responding.

Expert Judgment Expert judgment is use of subject matter experts about the effectiveness of the response forces. Since it is an opinion, based on a person’s background and experience, and individual bias, it can vary from expert to expert. The opinions of experts must be considered carefully, and if possible, tempered against insights from other experts and analysis methods. Expert judgment is difficult to verify and not always consistent or repeatable, and results can vary from site to site and even target to target based on the subject matter experts involved.

Mathematical Models

Mathematical models range from complex models to simple numerical models used to analyze specific factors in an engagement. As a general rule, mathematical models have a high level of abstraction, are flexible, produce consistent and repeatable results, and are relatively fast to use compared to other complex methods such as simulations. However, mathematical models have limitations. They only consider a few factors at a time, and do not address a number of factors that affect the outcome of an actual engagement such as stochastic effects, force density, force movement, terrain effects, tactical decision-making, and command, control, and communications. Simple numerical calculations are often used in place of or to augment expert judgment determinations. Simple numerical calculations include data tables, curve-fitted equations, continuous time Markov chain (CTMC) methods, and Monte Carlo methods. Mathematical models can serve a useful purpose, and provide general insights to support protection-planning activities involving response forces. In the late 1800s, Carl von Clausewitz studied European battles, and a book he wrote titled Vom Kriege (On War) was published after his death. Based on his analysis, he determined that it was nearly impossible for a force half the size of another to defeat it in battle, despite the tactics used by the smaller force. He states:

“If we strip the combat of all modifications which it may undergo according to its immediate purpose and the circumstances from which it proceeds…then there remains

21. Neutralization


only the bare conception of the combat, that is a combat without form, in which we distinguish nothing but the number of the combatants. From this we may infer, that it is very difficult in the present state of Europe, for the most talented general to gain a victory over an enemy double his strength.”

If this data is taken literally at a high level, it can be used to develop a simple numeric model to estimate PN. A data set can be built from Table 21-2:

Table 21-2. Simple Force Ratio Table for Estimating PN This obviously a very simple numerical method, and only accounts for numbers of combatants on each side. There are more sophisticated numerical models that have been developed, such as Lanchester’s laws. Frederick Lanchester was a mathematician and engineer who believed the advent of modern weapons made total numbers of combatants a factor less important than the number and type of weapons being used by one side against another. He developed two mathematical models that used functions of power (the rate a side can inflict casualties with its weapons) and time. The first law he developed is known as Lanchester’s Linear Law, and it was used to model ancient combat where a combatant can engage one opposing combatant at a time with a weapon (e.g., a sword or a spear). All things being equal, the greater the difference in the number of weapons (which in this case is equal to the number of combatants) between two armies, the greater the likelihood of winning the engagement. The second law he developed is known as Lanchester’s Square Law, and is still used today to model modern combat where a combatant can attack many opposing combatants and be engaged by many combatants at the same time. The rate of attrition now depends only on the number of weapons shooting, and the model determines the rate of attrition over time based on the square of the number of weapons being fired by a side.

Force Ratio PN 2 (2 to 1) 1.0 1 (1 to 1) 0.5 0.5 (1 to 2) 0.0

Simulations There are a number of simulation types that can be used to gather data to estimate PN for a given engagement. Militaries throughout the world use war games for planning and training purposes. War games are sometimes conducted on sand tables configured to have terrain similar to a real environment or on large maps spread on a table map or site schematic with either icons or figurines to represent combat elements. These types of exercises are known as “tabletop” exercises, and are a form of simulation. This method has been used in warfare at least since Roman Legion times and probably earlier. Commanders can place the icons in various positions



on the map and debate the outcome of possible engagements. A crucial element for tabletop analysis is the method used to determine the outcome of engagements. Expert judgment, data tables, or a set of rules with simple numerical calculations are the most common methods. Simulated physical engagements are known as force-on-force (FOF) exercises. Force-on-force exercises typically are conducted with three groups: mock adversaries, mock responders, and referees. Generally, the site will maintain on-duty response force personnel in the event an actual response is needed. These exercises require a significant amount of planning to make them as realistic as possible while meeting safety and other facility requirements and minimizing the impact to operations at a facility. They are generally the most expensive method in terms of personnel involved and are conducted infrequently. These types of simulations provide useful insights into the effectiveness of contingency plans, tactical decision-making by the response force personnel and commanders, command control and communication, and training levels of the response force. Statistically though, a system with a PN of 95% is expected to lose an average of 5 times out 100 over a large number of tests. Since not enough force-on-force tests are conducted to produce a probability of system win with a high confidence level, the results can be combined with those from other methods. For example, if only one exercise is completed and the response force loses the exercise, it means in a pure statistical sense that PN is likely less than 1.0. Computer simulations of many types have been developed to allow analyses similar to force-on-force exercises, but ones conducted in a simulated environment with simulated entities representing adversaries and response forces. These simulations range from relatively low fidelity, simulating some of the factors in an actual engagement such as weapons effects, troop movement, and terrain, to relatively high fidelity, with three dimensional terrain, and algorithms that calculate the ability to see, hear, move, take cover, and engage opposing forces with various weapons systems based on location on the terrain. Some high fidelity simulations are physics-based, with algorithms that compute the speed of a person based on their posture (running, walking, or crawling) the terrain, weight carried, and fatigue factors, and calculate the trajectory of a bullet fired from a rifle to determine whether it impacts a person from an opposing force, or even inadvertently a person from the same side (known as fratricide). They can be human-in-the loop simulations, in which operators are used to direct the actions of single or multiple entities in the simulation, essentially becoming the “brain” for the entities or pre-planned simulations with behavior-based models that allow an analyst to plan general scenarios, strategies, and actions of both forces and allow the simulated entities make on the fly decisions based on behavior models. In spite of their many limitations, simulations have the ability to measure many more factors than mathematical models, and use of several different types of simulations can in aggregate form an important part of developing

21. Neutralization


a PN estimate with some assurance.

Actual Engagements

Obviously, as with all systems, real tests performed a sufficient number of times under actual conditions are the best indicator of the system’s performance. Some security systems, such as security systems used in prisons to prevent escape by prisoners, get tested often, and mostly succeed but sometimes fail. Prisons use this information to understand strengths and weaknesses in their system and to address deficiencies in the system identified from successful escapes. Sometimes, even an escape attempt that was successful thwarted identifies areas where the system can be improved. As an example, we can consider the number of escape attempts from prisons around the world using a helicopter (Table 21-3). This seems to be a complex act, but data indicates it can have a reasonable probability of success; many prisons are working to implement PPS measures to reduce this type of threat.

Table 21-3. Prison Escape and Attempted Escape Data Using a Helicopter – 1971 to Present

Date Country # Escapees/Attempted

Date Country # Escapees/Attempted

1971 Mexico 3 2000 France 3

1973 Ireland 3 2001 France 1

1978 US 0/3 2001 France 3

1981 Canada 0/1 2002 Brazil 2

1983 Australia 0/3 2002 US 5

1985 US 3 2003 France 3

1986 France 1 2005 France 0

1986 US 2 2005 France 3

1986 Italy 2 2006 Greece 2

1987 UK 2 2007 Belgium 1

1988 US 3 2007 France 1

1989 US 0/1 2007 Belgium 1

1989 US 2 2009 Greece 2

1991 US 5 2009 France 2

1992 France 2 2009 Belgium 4

1992 France 0/2 2010 UK 0/1

1993 France 0/2 2012 Russia 1

1996 Chile 4 2013 Greece 0/1

1997 Netherlands 0/1 2013 Greece 0/1

1999 Australia 1 2013 Canada 2

2000 US 1 2014 Canada 3

In the nuclear security field, where the outcome of an attack is not an escaped prisoner, but the possibility of deaths and injuries on the adversary side, the response force side, and potentially site personnel, with facilities and assets that may be damaged, and loss of control of nuclear material or



radioactive contamination. Actual engagements have one big advantage; the outcome is a known fact. The nuclear industry is fortunate that malicious acts are very infrequent, but unlike prison data available for escapes using a helicopter, the lack of actual data requires planners to use a combination of other methods to determine if the response force at a nuclear facility has an acceptable PN.

21.7 Simple Numerical Method

Select a Methodology to

Estimate PN

To conduct an analysis whose purpose is to estimate the PN at a specific site for a given scenario, an analyst must select a method or combination o methods to use, and then gather the data necessary for the selected method(s). In this course, a simple numerical method will be used to illustrate the process for evaluating PN. Because it is a simple method, it only accounts for three factors: number of combatants on each side, weapon types, and response force time.

Required Data Calculating PN requires collection of data necessary for the selected method. In this method, the important information to gather includes:

Target Target Condition Facility Facility Condition Protection Strategy Number of Adversaries Adversary Weapons Number of Response Force RFTs for the Response Force (may be a series of times if individuals or

groups arrive at different Times)

Target and Facility Matrix

The condition of the facility and targets impacts characteristics of a PPS. For analysis purposes, an analysis should consider all the different facility and target conditions that result in changes to PPS performance values. As an example, under certain conditions at a nuclear power plant a fresh fuel vault may be open during a reactor refueling operation, and a response force unit stationed at the vault door. During normal operations the vault door remains closed, and a response unit is not at the vault. During the refueling operation, the delay facing adversaries is reduced because the vault door is opened, but the response is increased. It is useful to build a matrix of all possible combinations in order to perform a comprehensive analysis of the performance of the PPS under all conditions. Since the number of combinations may be large, some facilities develop a set of criteria to “bound” the problem and analyze a subset of the total number of facility/target combinations. An example of a Target/Facility Matrix is displayed in Table 21-4.

21. Neutralization


Table 21-4. Sample Target/Facility Matrix

Target Category Sab. Cons. Target Condition

Facility Facility Condition

Protection Strategy

Fresh Fuel

2 N/A In Transport Transit between Fuel Fab and Reactor Hall

N/A Containment

Fresh Fuel

2 N/A Storage Reactor Bldg. – Fresh Fuel Vault

Vault Secure Containment

Fresh Fuel

2 N/A In Movement Reactor Bldg. – Reactor Hall

Vault Open Containment

Reactor Fuel

3 (dose rate)

Significant offsite

In reactor core Reactor Bldg. – Reactor Core

Operation Denial

Spent Fuel

3 (dose rate)

Significant offsite

In spent fuel pool

Reactor Bldg. – Spent Fuel pool

Operation Denial

Response Force Times

Once the different Target/Facility combinations have been identified one or more than one can be analyzed. For each combination, response force times are acquired from performance tests. Response force times normally have some variance, so an average of the times is sometimes used. A better measure is a percentile, which is a value for which a percentage of the data points are smaller. As an example, a series of performance tests indicate a given response force element can respond in an average of 15 minutes. The fastest response time is 2 minutes, and the slowest response time is 28 minutes. A calculation of a 75th percentile of the data indicates that 75% of the time the response force unit arrives in 19 minutes, so this might be a conservative response time used for that unit for analysis purposes. Table 21-5 is an example of response force times for different response force units. Table 21-5. Sample Response Force Time Table

Scenario 1 Target: Fresh Fuel Category: 2 Condition: Storage

Location: Reactor Bldg. – Fresh Fuel Vault Facility Condition: Vault Secure Protection Strategy: Containment

Response Order

Response Force Number Weapons/Equipment Transport Deployment Time

1st Facility Guards, Posts

1 none foot 30 seconds

2nd Facility Guards, Patrols

2 handgun car 60 seconds

3rd Tactical Response Team 1

5 semi-automatic rifle tactical vehicle

600 seconds

4th Tactical Response Team 2

5 semi-automatic rifle tactical vehicle

900 seconds

Simple

Numerical Method –

For purposes of this course, a simple numerical method for calculating PN has been developed. This method only considers three factors: size of each force, weapons, and response force time. For conservatism, the method is based on an



“3 to 1 Data Table”

assumption that three response force members have a high PN against one adversary. This accounts for uncertainties regarding the use of surprise, use of force multipliers, and other factors that may give an advantage to the attackers. To use this method, compare the adversary capabilities and numbers to those of the response force. If:

1. If Adversary numbers, weapon types, and capabilities exceed Response Force numbers, weapon types, and capabilities, then assign PN = 0

2. If Response Force numbers, weapon types, and capabilities exceed Adversary numbers, weapon types, and capabilities, then PN = 1

3. If Response Force numbers exceed the Adversary numbers, and their weapon types and capabilities are roughly equal to Adversary weapon types and capabilities, then estimate PN from the Data Table.

To use the Data Table:

1. Identify the row for the number of adversaries taking part in the attack. 2. For response force units that arrive before the adversary timeline is

complete, identify PN from the column that corresponds to the number of Responders from the order they respond.

As an example, the response force is composed of seven personnel armed with handguns. The adversaries consist of eight personnel, three with rifles and four with handguns. Adversary numbers, weapon types, and capabilities exceed Response Force numbers, weapon types, and capabilities, so assign PN as 0. If the response force is composed of seven personnel armed with rifles, and the adversaries consist of three personnel with handguns, the Response Force numbers, weapon types, and capabilities exceed Adversary capabilities, so assign PN as 0. If the response force consists of seven personnel armed with rifles, and the adversaries consist of six personnel with rifles, use Table 21-6 to estimate PN.

21. Neutralization


Table 21-6. 3 to 1 Data Table

Sample PN Problem Using the 3 to 1 Table

Using the example data in the previous tables, we can construct a PN table as in the following example. The Facility Guards are not armed, so they are excluded from consideration, as are the second tactical response team who arrive 200 seconds after the adversary timeline. Using the data table, it indicates the first responding patrols have a PN of 0.23, and the arrival of the first Tactical Response Team increases PN to 0.96 (Table 21-7).

Sample Problem Data • Target: Fresh Fuel Category: 2 Condition: Storage • Location: Reactor Bldg. – Fresh Fuel Vault Facility Condition:

Vault Secure • Protection Strategy: Containment • Adversary: 1 with handgun, 2 with semi-automatic rifles • Adversary Task Time Remaining after CDP: 700 seconds

Table 21-7. Sample PN Table

Order of Response

Response Force with Equal Capacity

Number Cumulative Number

Time Remaining

PN

None Facility Guards, Posts

Not Applicable

1st Facility Guards, Patrols

2 2 700 - 60 = 640 seconds

.23

2nd Tactical Response Team 1

5 7 700 - 600 = 100 seconds

.96

3rd Tactical Response Team 2*

5 12 700 - 900 = - 200 seconds



21.8 Summary

PPS effectiveness is the product of two probabilities: PI and PN. PI, determined from timely detection, is a measure of the effectiveness of the system detection and delay along a path. PI describes only the cumulative probability that the adversary may be interrupted. This metric alone does not answer the question of who wins—the response force or the adversary? PN is the measure of effectiveness of the response force against the adversary, independent of PI. PN is a complicated problem with many factors that determine its true value. Some of these factors can be quantified and measured, some can be qualitatively measured, and some are very difficult or impossible to measure. Four methods were described that can be used to estimate PN: expert opinion, mathematical models, simulations, and evaluations of actual attacks. Each method has advantages and disadvantages, and use of a combination of a number of methods helps estimate a value for PN with a greater degree of certainty, although no method or methods will predict the outcome of a single engagement.

21. neutralization analysis - sandia.gov · 21. neutralization analysis abstract. response, along...

Documents