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- Quantitative
- Risk Analysis
- Sanjay Goel
- University at Albany, SUNY
- Fall 2004

Course Outline

- gt Unit 1 What is a Security Assessment?
- Definitions and Nomenclature
- Unit 2 What kinds of threats exist?
- Malicious Threats (Viruses Worms) and

Unintentional Threats - Unit 3 What kinds of threats exist? (contd)
- Malicious Threats (Spoofing, Session Hijacking,

Miscellaneous) - Unit 4 How to perform security assessment?
- Risk Analysis Qualitative Risk Analysis
- Unit 5 Remediation of risks?
- Risk Analysis Quantitative Risk Analysis

Quantitative Risk AnalysisOutline for this unit

- Module 1 Quantitative Risk Analysis and ALE
- Module 2 Risk Aggregation
- Module 3 Case Study
- Module 4 Cost Benefit Analysis and Regression

Testing - Module 5 Modeling Uncertainties

Module 1Quantitative Risk Analysis and ALE

Quantitative Risk Analysis and ALEOutline

- What is Risk Analysis?
- What is Quantitative Risk Analysis?
- What are the steps involved?
- How to determine the Likelihood of Exploitation?
- How to determine Risk Exposure?
- How to compute Annual Loss Expectancy (ALE)?
- Examples
- Gym Locker
- Hard Drive Failure
- Virus Attack

Quantitative Risk Analysis and ALERisk Analysis

Definition

- Risk analysis involves the identification and

assessment of the levels of risks calculated from

the known values of assets and the levels of

threats to, and vulnerabilities of, those assets. - It involves the interaction of the following

elements - Assets
- Vulnerabilities
- Threats
- Impacts
- Likelihoods
- Controls

Quantitative Risk Analysis and ALERisk Analysis

Concept Map

- Threats exploit system vulnerabilities which

expose system assets. - Security controls protect against threats by

meeting security requirements established on the

basis of asset values.

Source Australian Standard Handbook of

Information Security Risk Management HB231-2000

Quantitative Risk Analysis and ALEQuantitative

Risk Analysis

- Quantitative risk analysis methods are based on

statistical data and compute numerical values of

risk - By quantifying risk, we can justify the benefits

of spending money to implement controls. - It involves three steps
- Estimation of individual risks
- Aggregation of risks
- Identification of controls to mitigate risk

Quantitative Risk Analysis and ALERisk Analysis

Steps

- Security risks can be analyzed by the following

steps - Identify and determine the value of assets
- Determine vulnerabilities
- Estimate likelihood of exploitation
- Compute frequency of each attack (with w/o

controls) using statistical data - Compute Annualized Loss Expectancy
- Compute exposure of each asset given frequency of

attacks - Survey applicable controls and their costs
- Perform a cost-benefit analysis
- Compare exposure with controls and without

controls to determine the optimum control

Quantitative Risk Analysis and ALEDetermining

Assets and Vulnerabilities

- Identification of Assets and Vulnerabilities is

the same for both Qualitative and Quantitative

Risk Analysis - The differences in both of these is in terms of

valuation - Qualitative Risk Analysis is more subjective and

relative - Quantitative Risk Analysis is based on actual

numerical costs and impacts.

Quantitative Risk Analysis and ALEDetermine

Likelihood of Exploitation

- Likelihood relates to the stringency of existing

controls - i.e. likelihood that someone or something will

evade controls - Several approaches to computing probability of an

event - classical, frequency and subjective
- Probabilities hard to compute using classical

methods - Frequency can be computed by tracking failures

that result in security breaches or create new

vulnerabilities can be identified - e.g. operating systems can track hardware

failures, failed login attempts, changes in the

sizes of data files, etc. - Difficult to obtain frequency of attacks using

statistical data.Why? - Data is difficult to obtain often inaccurate
- If automatic tracking is not feasible, expert

judgment is used to determine frequency

Quantitative Risk Analysis and ALEApproaches

- Delphi Approach
- Probability in terms of integers (e.g. 1-10)
- Normalized
- Probability in between 0 (not possible) and 1

(certain)

Quantitative Risk Analysis and ALEDelphi Approach

- Subjective probability technique originally

devised to deal with public policy decisions - Assumes experts can make informed decisions
- Results from several experts analyzed
- Estimates are revised until consensus is reached

among experts

Frequency Ratings

More than once a day 10

Once a day 9

Once every three days 8

Once a week 7

Once in two weeks 6

Once a month 5

Once every four months 4

Once a year 3

Once every three years 2

Less than once in three years 1

Quantitative Risk Analysis and ALERisk Exposure

- Risk is usually measured as per annum and is

quantified by risk exposure. - ALE (Annual Loss Expectancy, expressed as

/year) - If an event is associated with a loss
- LOSS RISK IMPACT ()
- The probability of an occurrence is in the range

of - 0 (not possible) and 1 (certain)
- Quantifying the effects of a risk by multiplying

risk impact by risk probability yields risk

exposure. - RISK EXPOSURE RISK IMPACT x RISK PROBABILITY

Quantitative Risk Analysis and ALEIntangible

Assets

- Incorporating intangible assets within

Quantitative Risk Analysis is difficult as it is

hard to put a price on things such as trust,

reputation, or human life. - However, it is necessary to put an as accurate a

value as possible when factoring these assets

within risk analysis as they may be even more

important than tangible assets.

Quantitative Risk Analysis and ALEComputing ALE

- Single Loss Expectancy Loss to an asset if event

occurs - Value of the lost asset Ci
- Impact on the Asset (if event occurs) Pi
- SLE Ci Pi
- Annualized Rate of Occurrence (ARO)

characterizes, on an annualized basis, the

frequency with which a threat is expected to

occur. - Annualized Loss Expectancy (ALE) computes risk

using the probability of an event occurring over

one year. - Formulation
- ALE (SLE)(ARO)
- Source Handbook of Information Security

Management, Micki Krause and Harold F. Tipton

Quantitative Risk Analysis and ALEExample 1

Gym Locker

- Scenario There is a gym locker used by its

members to store clothes and other valuables. The

lockers cannot be locked, but locks can be

purchased. - You need to determine
- Risk exposure for gym members
- Controls to reduce risk

Quantitative Risk Analysis and ALEExample 1

Gym Locker, contd.

- Identify assets and determine value
- Clothes 50
- Wallet 100
- Glasses 100
- Sports equipment 30
- Drivers license 20
- Car keys 100
- House keys 60
- Tapes and walkman 40
- ____
- Total Loss/week 500
- Find vulnerability
- Theft
- Accidental loss
- Disclosure of information (e.g. read wallet)
- Vandalism

Quantitative Risk Analysis and ALEExample 1

Gym Locker, contd.

- Estimate likelihood of exploitation
- 10 (more than once a day)
- 9 (once a day)
- 7 (once a week)
- 6 (once every two weeks)
- 5 (once a month)
- For theft estimated likelihood is 7
- Figure annual loss
- 500 worth of loss each week, 52 weeks in a

year - 26,000 loss per year

- 4 (once every four months)
- 3 (once a year)
- 2 (once every three years)
- 1 (less than once every 3 years)

Quantitative Risk Analysis and ALEExample 1

Gym Locker, contd.

- Determine cost of added security
- New lock 5
- Replacement for lost key 10
- On average members lose one key twice a month (24

times per year) - Estimate likelihood of exploitation under added

security - The new likelihood of theft could be estimated at

a 4. - Cost Benefit Analysis
- Revised Losses (including cost of controls)
- (500 4) (1524) 2360
- Net savings 26000 2360 23640

Quantitative Risk Analysis and ALEExample 2

Hard Drive Failure

- The chance of your hard drive failing is once

every three years - Probability 1/3
- Intrinsic Cost
- 300 to buy new disk
- Hours of effort to reload OS and software
- 10 hours
- Hours to re-key assignments from last backup
- 4 hours
- Pay per hour of effort
- 10.00 per hour
- Total loss (risk impact)
- 300 10 x (104) 440
- Annual Loss Expectancy (pa per annum)
- (440 x 1/3)pa 147 pa

Quantitative Risk Analysis and ALEExample 3

Virus Attack

- Situation Virus Attack on same system
- You frequently swap files with other people, but

have no anti-virus software running. - Assume an attack every 6 months (Probability 2

per year) - No need to buy a new disk
- Rebuild effort (10 4) hours
- Total loss 10 x (10 4) 140
- ALE (140 x 2) pa 280 pa

Quantitative Risk Analysis and ALE Questions 1

and 2

- Why is it important to quantify risk?
- Give the definitions for
- Single Loss Expectancy
- Annualized Rate of Occurrence
- Annual Loss Expectancy

Quantitative Risk Analysis and ALE Question 3

- For this situation
- Same system as examples 2 and 3

Module 2Risk Aggregation

Risk AggregationOutline

- How do you determine risk posture?
- What is this risk aggregation model?
- Matrices
- Asset/Vulnerability
- Vulnerability/Threat
- Threat/Control

Risk AggregationRisk Posture

- Individual risks aggregated Total risk posture
- True comparison of relative risks of different

organizations - Mathematical approach for aggregation provided
- Methodology standardized
- Data needs to be customized to organization
- Controls can reduce the cost of exposure
- Need to determine optimum controls for

organization - Methodology for determining controls shown next

slide - Analysis should be undertaken to see the impact

of new projects on security

Risk AggregationModel

- Let
- A be a vector of loss of an asset where al is the

lth asset, s.t., 0 lt l lt L - V be a vector of vulnerabilities where vk is the

kth vulnerability, s.t., 0 lt k lt K - T be a vector of threats where tj is the jth

asset, s.t., 0 lt j lt J - C be the vector of vulnerabilities where ci is

the ith control, s.t., 0 lt i lt I - Also Ma be the matrix that defines the impact of

vulnerabilities (breach in security) on assets,

where, akl is the impact of kth vulnerability on

the lth asset - Also Mß be the matrix that defines the impact of

threats on the vulnerabilities, where, ßjk is the

impact of jth threat on kth vulnerability - Also M? be the matrix that defines the impact of

a controls (breach in security) on the threats,

where, ?ij is the impact of ith control on the

jth threat

The notation is graphically explained in the next

few slides

Risk AggregationModel, contd.

A (Assets)

- Data Collection
- Primary Data from corporations that track

financial losses due to different attacks - Secondary Data from the reports of financial loss

from organizations like CERT, CSI/FBI and AIG - Data specific to a corporation, could perhaps be

classified into different groups of companies

akl

V (Vulnerabilities)

L

K

- Where akl is the Impact of vulnerability k on

given asset l. - i.e. fraction of the asset value that will be

lost if the vulnerability is exploited

Risk AggregationModel, contd.

V (Vulnerabilities)

- Data Collection
- Threat data and frequency of threats is

information that is routinely collected in CERT

and other such agencies. - Log data and collected data from the organization

itself can be another source of information - Data can also be collected via use of automated

monitoring tools

bjk

T (Threats)

K

J

bjk is the probability that threat j will exploit

vulnerability k

Risk AggregationModel, contd.

T (Threats)

- Data Collection
- Approximate control data can be procured from

various industry vendors who have done extensive

testing with tools. - Other sources of data can be independent agencies

which do analysis on tools.

gij

C (Controls)

J

I

gij is the fraction by which controls reduce the

frequency of a threat exploiting a vulnerability

Risk AggregationModel, contd.

Then losses if no control exist

Then losses if controls exist

- sum
- ? product

Risk AggregationOptimization

If ? is the maximum allocated budget for controls

the optimization problem can be formulated as

Risk AggregationQuestion 1

- How would you collect data for the following
- Assets and Values
- Potential Threats
- Exploitable Vulnerabilities
- Possible Controls

Module 3Case Study

Case StudyOutline

- What is the case about?
- What would fit into the categories of
- Assets
- Vulnerabilities
- Threats
- Controls
- Filling in the matrices
- Asset/Vulnerability
- Vulnerability/Threat
- Threat/Control

Case StudyExample

- Use the information that you have learned in the

lecture in the following case study of a

government organization. - Remember these key steps for determining ALE
- Identify and determine the value of assets
- Determine vulnerabilities
- Estimate likelihood of exploitation
- Compute ALE
- Survey applicable controls and their costs
- Perform a cost-benefit analysis

Case StudyCase

An organization delivers service throughout New

York State. As part of the planning process to

prepare the annual budget, the Commissioner has

asked the Information Technology Director to

perform a risk analysis to determine the

organizations vulnerability to threats against

its information assets, and to determine the

appropriate level of expenditures to protect

against these vulnerabilities. The organization

consists of 4,000 employees working in 200

locations, which are organized into 10 regions.

The average rate of pay for the employees is

20/hr. Cost benefit analysis has been done on

the IT resource deployment, and the current

structure is the most beneficial to the

organization, so all security recommendations

should be based on the current asset

deployment. Each of the 200 locations has

approximately 20 employees using an equal number

of desktop and laptop computers for their

fieldwork. These computers are used to collect

information related to the people served by the

organization, including personally identifying

information. Half of each employees time is

spent collecting information from the clients

using shared laptop computers, and half is spent

processing the client information at the field

office using desktop computers. Replacement cost

for the laptops is 2,500 and for the desktop is

1,500. Each of the 10 regions has a network

server, which stores all of the work activities

of the employees in that region. Each server will

cost 30,000 to replace, plus 80 hours of staff

time. Each incident involving a server costs the

organization approximately 1,600 in IT staff

resources for recovery. Each incident where

financial records or personal information is

compromised costs the organization 15,000 in

lawyers time and settlement payouts. Assume that

the total assets of the organization are worth 10

million dollars. The organization has begun

charging fees for the public records it collects.

This information is sold from the organization

website at headquarters, via credit card

transactions. All of the regional computers are

linked to the headquarters via an internal

network, and the headquarters has one connection

to the Internet. The headquarters servers query

the regional servers to fulfill the transactions.

The fees collected are approximately 10,000 per

day distributed equally from each region, and the

transactions are uniformly spread out over a 24

hour period.

Case StudyExample- Assets (Tangible)

- Transaction Revenue- amount of profit from

transactions - Data- client information
- Laptops- shared, used for collecting information
- Desktops- shared, used for processing client

information - Regional Servers- stores all work activities of

employees in region - HQ Server- query regional servers to fulfill

transactions

Case StudyExample- Asset Valuations (Cost per

Day)

Transaction Revenue 10,000 per day Data

(Liability) 10 million (total assets of

organization) Laptops ½ x 200 (locations) x

20 (employees) x 2,500 (laptop cost)

5,000,000 Desktops ½ x 200 (locations) x 20

(employees) x 1,500 (desktop cost)

3,000,000 Regional Servers 30,000 (server

cost)x 10 (regions) 80 (hours) x 20 (pay

rate) x 10 (regions) 10,000 (transaction

revenue) 326,000 HQ Server 10,000

(transaction revenue) 100,000 (cost of HQ

server) 80 (hours) x 20 (pay rate) x 10

(regions) 126,000

Case StudyExample- Vulnerabilities

- Vulnerabilities are weaknesses that can be

exploited - Vulnerabilities
- Laptop Computers
- Desktop Computers
- Regional Servers
- HQ server
- Network Infrastructure
- Software
- Computers and Servers are vulnerable to network

attacks such as viruses/worms, intrusion

hardware failures - Laptops are especially vulnerable to theft

Case StudyExample- Threats

- Threats are malicious benign events that can

exploit vulnerabilities - Several Threats exist
- Hardware Failure
- Software Failure
- Theft
- Denial of Service
- Viruses/Worms
- Insider Attacks
- Intrusion and Theft of Information

Case StudyExample- Controls

- Intrusion detection and firewall upgrades on HQ

Server - mitigate HQ server failure and recovery
- Anti-Virus Software
- mitigates threat of worms, viruses, DOS attacks,

and some intrusions - Firewall upgrades
- mitigates threats of DOS attacks and some

intrusions, worms and viruses - Redundant HQ Server
- reduces loss of transaction revenue
- Spare laptop computers at each location
- reduces loss of transaction revenue and

productivity - Warranties
- reduces loss of transaction revenue and cost of

procuring replacements - Insurance
- offset cost of liability
- Physical Controls
- reduce probability of theft
- Security Policy
- can be used to reduce most threats.

Case StudyAsset/Vulnerability Matrix

- The coefficients of this matrix are usually based

on internal data as well as financial loss

organizations - For the current example we will assume data for

illustration of the concept - Transactions are mostly associated with the

regional servers which store the data, the HQ

server which takes all requests, and the network

infrastructure with which clients access the

data. (.30 each) - Laptops, desktops and software is only associated

with the remaining 10 (.033 each) - Data that is located on laptops and desktops make

up only 10 of total data because they are only

used for collecting and processing. - The regional servers contain all other data.
- Other assets are associated at 100 with their

respective vulnerabilities. (e.g. laptops with

laptops, desktops with desktops, etc.)

Case StudyAsset/Vulnerability Matrix, contd.

Assets Vulnerabilities Transaction Revenue Data (Liability) Laptops Desktops Regional Servers HQ Server Aggregates (Impact)

Input Asset Values ? 10,000 10,000,000 5,000,000 3,000,000 326,000 126,000 S (asset value x vulnerability)

Laptops .033 .05 1 0 0 0 5,500,330

Desktops .033 .05 0 1 0 0 3,500,330

Regional Servers .30 .90 0 0 1 0 9,329,000

HQ Servers .30 0 0 0 0 1 129,000

Network Infrast. .30 0 0 0 0 0 3000

Software .033 0 0 0 0 0 330

- Customize matrix to assets vulnerabilities

applicable to case - Compute cost of each asset and put them in the

value row - Determine correlation with vulnerability and

asset - Compute the sum of product of vulnerability

asset values add to impact column

Case StudyVulnerability/Threat Matrix

- The coefficients of this matrix are usually based

on data from the literature, e.g., - if rate of failure of hardware is rf (per unit

time) - the number of pieces of hardware is n then
- the total number of failed components during a

time period is rfn - the fraction of hardware that fails is rfn/n rf
- For the current example we will assume data for

illustration of the concept - Failure rate of laptops is .001 per day (i.e.,

one in a thousand laptops encounters hardware

failure during a day) - Similarly failure rate of a desktop is .0002

(i.e. 2 in ten thousand desktops would encounter

hardware failure in a given day. - Hardware failure can cause loss of software,

however, our assumption is that all software is

replaceable from backups

Case StudyVulnerability/Threat Matrix, contd.

- We assume that the hardware failure will disrupt

the network once every one hundred days - There is 0.3 percent chance that software failure

can lead to failure of desktops - We assume that there is a .01 chance of a laptop

being stolen, .001 for a desktop, and .0002 for

servers. - There is a very low chance that network equipment

is stolen since it is kept in secure rooms

(.0001) - When equipment is stolen some software may have

been stolen as well - We assume that denial-of-service is primarily

targeted at servers and not individual machines - We assume that the denial-of-service can disable

machines as well as cause destruction of software

- Insider attacks are primarily meant to exploit

data disable machines - We assume that the servers have less access thus

are less vulnerable to insider attacks

Case StudyVulnerability/Threat Matrix, contd.

Vulnerabilities Threats Laptops Desktops Regional Servers HQ Servers Network Infrast. Software Aggregates (Threat Importance)

Input Impact Aggregates? 5,500,330 3,500,330 9,329,000 129,000 3,000 330 S (impact value x threat value)

Hardware Failure .001 .0002 .0002 .0002 .01 0 8,122.00

Software Failure .003 .003 .003 .003 0 0 55,375.98

Equipment Theft .0160 .001 .0002 .0002 .0001 .005 93,399.16

Denial of Service .0001 .0001 .001 .001 0 0 10,358.07

Viruses/Worms .003 .003 .003 .003 0 .001 55,376.31

Insider Attacks .001 .001 .0001 .0001 .0001 .001 9,947.09

Intrusion .001 .001 .001 .001 0 .001 18,458.99

- Complete matrix based on the specific case
- Add values from the Impact column of the previous

matrix - Determine association between threat and

vulnerability - Compute aggregate exposure values by multiplying

impact and the associations

Case StudyThreat/Control Matrix

- Some of these controls have threats associated

with them. However, these are secondary

considerations and we will be focusing on primary

threats. - We assume that IDS systems will control 30 of

the DOS attacks, 30 of Viruses and Worms and 90

of intrusions - In addition, IDS systems do not impact insider

attacks - Anti-Virus Software will prevent 90 of Viruses

and Worms. - That upgrades to a firewall will greatly control

(90 each) of DOS attacks, as well as Viruses and

Worms. It will control 30 of intrusions, but not

insider attacks. - A redundant HQ server will control 10 of

hardware failure (when the original HQ server

fails). This is the same percentage for theft and

insider attacks. - Also, a redundant HQ server will help with 80 in

cases of DOS attacks on the HQ server. - Spare laptops will assist in cases of hardware

failure and theft (30 because of volume).

Case StudyThreat/Control Matrix, contd.

- We assume that warranties will help with 70 of

both hardware failure and software failure. While

it will assist with the cost of new hardware or

software, will not reduce employee time. - It is determined that insurance will be able to

control 90 of impacts from the threats of theft,

DOS attacks, Virus/Worm attacks, Insider Attacks,

and Intrusion. - Physical controls (locks, key cards, biometrics,

etc.) will control 90 of theft. - Also, it is assumed that a security policy will

assist with 20 of all threats since every policy

can have procedures which can assist in

prevention. - Customize matrix based on the specific case
- Add values from the threat importance column of

the previous matrix - Determine impact of different controls on

different threats - Multiply (1-impact) throughout threat column and

multiply to threat importance to get values.

Case StudyThreat/Control Matrix, contd.

Threats Controls Hardware Failure Software Failure Theft Denial of Service Viruses/ Worms Insider Attacks Intrusion Aggregates

Input Threat Importance Values? 8,122.00 55,375.98 93,399.16 10,358.07 55,376.31 9,947.09 18,458.99 S (threat importance x impact of controls)

Intrusion Detection 0 0 0 .30 .30 0 .90 36,333.41

Anti-Virus 0 0 0 0 .90 0 0 49,838.68

Firewall Upgrades 0 0 0 .90 .90 0 .30 64,698.64

Redundant HQ Server .10 0 .10 .80 0 .10 0 19,433.28

Spare Laptops .30 0 .30 0 0 0 0 30,456.35

Warranties .70 .70 0 0 0 0 0 44,448.59

Insurance 0 0 .90 .90 .90 .90 .90 168,785.66

Physical Controls 0 0 .90 0 0 0 0 84,059.24

Security Policy .20 .20 .20 .20 .20 .20 .20 50,207.52

Calculate Exposure with Controls ? 1,228.05 13,290.24 470.73 11.60 31.01 716.19 103.37

Case StudyAssignment

- Given the matrices and the example case provided,

use this same methodology in application to

determine the information security risk in your

own organization.

Module 4Cost Benefit Analysis Regression

Testing

Cost Benefit Analysis Regression TestingOutline

- How to use matrices for cost benefit analysis?
- How to calculate Risk Leverage?
- Applying the case study example
- Examples
- Unauthorized Access
- Graphical Cost Benefit Analysis with Regression

Testing

Cost Benefit AnalysisMatrix Cost Benefit Analysis

- The exposure before controls is equal to the

summation of the aggregate values for impact

value x threat value. (Vulnerability/Threat

Matrix) - In this case, the value is equal to 251,037.60

- The exposure after controls is equal to the sum

of all of the multiplied threat importance

values. - For example, in the Hardware Failure column, we

will take each of the threat importance values

and subtract them each from 1. These values

should be multiplied together. (Threat/Control

Matrix) - This will give us (1-.10) x (1 - .30) x (1 -

.70) x (1 - .20) 0.15 - This value will be multiplied by the threat

importance value 0.15 x 8,122.00 1,218.30

(cost with controls of Hardware Failure) - Do this for all Threat columns and then summate

all the values. - This value is equal to 15,851.19

Cost Benefit Analysis Risk Leverage

- Costs are associated with both
- Potential Risk Impact
- Reducing Risk Impact
- Risk Leverage is the difference in risk exposure

divided by the cost of reducing the risk - Let
- rf be the risk exposure after imposing controls
- ri be the risk exposure prior to imposing

controls - c be the cost of controls
- Leverage l (ri-rf)/c
- This tells you how many times the reduction in

risk exposure is greater then the cost of

controls.

Cost Benefit Analysis Matrix Example

- We are using this equation to calculate cost
- Ci Csi Cri x t
- Where Ci is the total cost of control i.
- Csi is the static (one-time) cost of the control.
- Cri is the additional cost per day (maintenance,

updates, etc.) for the control. - t is equal to time (if calculating for a year,

would equal 365). - We are assuming cost of control values for this

example - Intrusion Detection 21,000 x 11 160 x 11 x

365 873,400 - Anti-Virus 1,876 x 4,000 (laptops desktops)

1,876 x 11 (number of servers) 7,524,636 11

x 160 x 365 8,167,036 - Firewall Upgrades 10,000 x 211 160 x 211

2,143,760 - Redundant HQ Server 100,000 160 x 365

158,400 - Spare Laptops 2,500 x 200 500,000
- Warranties (3 year) 100 x 4,000 (laptops

desktops) 1000 x 10 (regional servers)

1,200 (HQ Server) 411,200 - Insurance 5,000,000 (per 365 days)
- Physical Controls 5,000 x 211 160 x 211 x

365 13,377,400 - Security Policy (creation, implementation,

enforcement) 640 x 365 233,600

Cost Benefit Analysis Matrix Example

- Leverage l (ri-rf)/c
- ri 251,037.60 x 365 91,628,724
- rf 15,851.19 x 365 5,785,684.35
- C 30,864,796
- 251,037 15,851.19 / 30,864,796 .008
- 91,628,724 - 5,785,684.35 / 30,864,796 2.78
- The reduction in risk exposure is almost 3x

greater than the cost of controls

Cost Benefit AnalysisExample 4 Unauthorized

access

- Scenario A company uses a common carrier to link

to a network for certain computing applications.

The company has identified the risks of

unauthorized access to data and computing

facilities through the network. These risks can

be eliminated by replacement of remote network

access with the requirement to access the system

only from a machine operated on the company

premises. The machine is not owned a new one

would have to be acquired.

Cost Benefit AnalysisExample 4 Unauthorized

Access

Cost/Benefit Analysis for Replacing Network Access

Item Amount

Risk unauthorized access and use Risk unauthorized access and use

Access to unauthorized data and programs 100,000 _at_ 2 likelihood per year 2,000

Unauthorized use of computing facilities 10,000 _at_ 40 likelihood per year 4,000

Expected annual loss (2,000 4,000) 6,000

Effectiveness of network control 100 -6,000

Cost Benefit AnalysisExample 4 Unauthorized

Access

Network Control cost Network Control cost

Hardware (50,000 amortized over 5 years) 10,000

Software (20,000 amortized over 5 years) 4,000

Support personnel (each year) 40,000

Annual cost 54,000

Expected annual loss (6,000 6,000 54,000) 54,000

Savings (6,000 54,000) -48,000

Regression TestingExample 5 Graphical Cost

Benefit Analysis

- Scenario This is a case where use of regression

testing is being considered after making an

upgrade to fix a security flaw. We want to

determine if regression testing is economical in

this scenario. - Regression Testing means applying tests to verify

that all remaining functions are unaffected by

the change. - Lets refer to the diagram on the following slide,

to compare the risk impact of doing regression

testing with not doing it. - Upper part of the diagram
- the risk of conducting regression testing
- Lower part of the diagram
- shows the risks of not doing regression testing

Regression TestingExample 5 Cost Savings

- In the two cases, one of three things can happen

if regression is done - We find a critical fault
- We miss finding the critical fault
- There are no critical faults to be found.
- For each possibility
- Calculate the probability of an unwanted outcome,

P(UO). - Associate a loss with that unwanted outcome,

L(UO).

Regression TestingExample 5 Calculation

In our example, if we do regression testing and

miss a critical fault in the system (a

probability of 0.05), the loss could be 30

million. Multiplying the two, we find the risk

exposure for that strategy to be 1.5 million. As

the calculations in the figure prove, it is much

safer to do regression testing than to skip it.

Combined Risk Exposure

Cost Benefit Analysis and Regression Testing

Questions 1 and 2

- What is regression testing?
- What is the calculated risk exposure for not

doing a regression testing, if finding a critical

fault has a probability of 0.35 and the loss is

estimated at 4.5 million dollars.

Cost Benefit Analysis Regression

TestingAssignment

- Do a cost benefit analysis based on the matrix

that you have created for your own organization.

Module 5Modeling Uncertainties

Modeling UncertaintiesOutline

- How do you model?
- Monte Carlo Simulation
- What is the approach?
- How to model valuation of assets?
- How to model frequency of threats?
- How to model impact of threats?
- How to model controls?
- How to model distribution of risk exposure?
- How to perform a sensitivity analysis for risk

exposure?

Modeling UncertaintiesModeling Uncertainties

- Uncertainty exists regarding value that should be

assumed by one or more independent variables in

the Risk Model. - Contributions to the models uncertainty
- Lack of knowledge about particular values
- Knowledge that some values might always vary
- If it cannot be determined with certainty what

value one or more input variables in a model will

assume, this uncertainty is naturally reflected

on the outcome of the dependent variable(s). - The risk metric is
- not determined by the value of its independent

variables (asset values and vulnerabilities,

frequency and impact of threats) - a function of the probability distribution of

each of these random variables - A good approach to dealing with uncertainty gtgt

simulation

Modeling Uncertainties Monte Carlo Simulation

Approach

- The approach follows the following steps
- Develop risk model
- Define the shape and parameters of probability

distributions of each input variable - Run Monte Carlo simulation
- Build histogram for dependent variables in the

model (risk and updated risk) - Compute summary statistics for dependent

variables in model - Perform sensitivity analysis to detect

variability sources - Analyze potential dependency relationships among

variables in model

Modeling Uncertainties Monte Carlo Simulation

Value of Assets

Truncated Normal Distribution(mean 50)

- Asset values here are samples and do not

represent collected data - In real cases real assets of the organization

need to be identified - Value needs to be assigned to the assets

Modeling Uncertainties Monte Carlo Simulation

Frequency of Threats

- Annualized frequency of threats is required to

compute the annualized loss expectancy. - This data can be collected from several sources
- Tracking and collecting data from Internal logs
- Report from agencies such as CERT

Modeling Uncertainties Monte Carlo Simulation

Impact of Threats

Triangular distribution (mode, max1, min0)

Modeling Uncertainties Monte Carlo Simulation

Controls

Triangular distribution( mode, max1, min0)

Modeling Uncertainties Monte Carlo Simulation

Risk Exposure Distribution

Cumulative Distribution

Modeling Uncertainties Monte Carlo Simulation

Reduced Risk Exposure

Cumulative Distribution

Modeling Uncertainties Monte Carlo Simulation

Sensitivity Analysis

Modeling UncertaintiesQuestions 1 and 2

- Why does uncertainty exist within risk analysis?
- Describe the approach towards Monte Carlo

Simulation.

Modeling UncertaintiesAssignment

- Using the data provided in the case study, or

your own risk analysis, use Monte Carlo

Simulation to provide a graphical display.

Appendix

Quantitative AnalysisSummary

- Risk Exposure
- RISK EXPOSURE RISK IMPACT x RISK PROBABILITY
- Annual Loss Expectancy (ALE)
- Identify and determine the value of assets
- Determine vulnerabilities
- Estimate likelihood of exploitation
- Compute ALE
- Survey applicable controls and their costs
- Perform a cost-benefit analysis

Quantitative AnalysisSummary Contd.

- Risk Aggregation
- Optimization
- simple formulation
- Cost Benefit Analysis
- LEVERAGE (RISK EXPOSUREbefore reduction

RISK EXPOSUREafter reduction)

________________________________________________

COST OF REDUCTION - Regression Testing
- Used for comparing risk impact
- Monte Carlo Simulation
- 1)Develop risk model, 2) Define the shape and

parameters, 3)Run simulation, 4)Build histogram,

5)Compute summary statistics, 6)Perform

sensitivity analysis, 7)Analyze potential

dependency relationship

Acknowledgements Grants Personnel

- Support for this work has been provided through

the following grants - NSF 0210379
- FIPSE P116B020477
- Damira Pon, from the Center of Information

Forensics and Assurance contributed extensively

by reviewing and editing the material - Robert Bangert-Drowns from the School of

Education provided extensive review of the

material from a pedagogical view.