Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 1 Unit Outline Qualitative Risk Analysis Module 1: Qualitative Risk Analysis Module 2: Determine Assets and Vulnerabilities Module 3: Determine Threats and Controls  Module 4: Matrix Based Approach Module 5: Case Study Module 6: Summary

Module 4 Matrix Based Approach

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 3 Students should be able to: –Understand how to determine risk posture. –Comprehend a risk aggregation model. –Recognize the need for optimization of risk. –Use the matrix-based approach to determine risk Matrix Based Approach Learning Objectives

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 4 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 Matrix-Based Approach Risk Posture

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 5 Let: –A be a vector of loss of an asset where a l is the l th asset, s.t., 0 < l < L –V be a vector of vulnerabilities where v k is the k th vulnerability, s.t., 0 < k < K –T be a vector of threats where t j is the j th asset, s.t., 0 < j < J –C be the vector of controls where c i is the i th control, s.t., 0 < i < I –Also M α be the matrix that defines the impact of vulnerabilities (breach in security) on assets, where, α kl is the impact of k th vulnerability on the l th asset –Also M β be the matrix that defines the impact of threats on the vulnerabilities, where, β jk is the impact of jth threat on kth vulnerability –Also M γ be the matrix that defines the impact of a controls (breach in security) on the threats, where, γ ij is the impact of i th control on the j th threat Matrix-Based Approach Model The notation is graphically explained in the next few slides

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 6 Matrix-Based Approach Model, cont’d.  kl A (Assets) V (Vulnerabilities) Where  kl 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 L K 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

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 7 Matrix-Based Approach Model, cont’d.  jk V (Vulnerabilities) T (Threats)  jk is the probability that threat j will exploit vulnerability k K J 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

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 8 Matrix-Based Approach Model, cont’d.  ij T (Threats) C (Controls)  ij is the fraction by which controls reduce the frequency of a threat exploiting a vulnerability J I 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.

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 9 Then losses if no control exist Matrix-Based Approach Model, cont’d. Then losses if controls exist  = sum  = product

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 10 Risk Aggregation Optimization If ζ is the maximum allocated budget for controls the optimization problem can be formulated as:

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 11 Consists of three matrices –Vulnerability Matrix: Links assets to vulnerabilities –Threat Matrix: Links vulnerabilities to threats –Control Matrix: Links threats to the controls Step 1 –Identify the assets & compute the relative importance of assets Step 2 –List assets in the columns of the matrix. –List vulnerabilities in the rows within the matrix. –The value row should contain asset values. –Rank the assets based on the impact to the organization. –Compute the aggregate value of relative importance of different vulnerabilities Matrix Based Approach Methodology

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 12 Step 3 –Add aggregate values of vulnerabilities from vulnerability matrix to the column side of the threat matrix –Identify the threats and add them to the row side of the threat matrix –Determine the relative influence of threats on the vulnerabilities –Compute aggregate values of importance of different threats Step 4 –Add aggregate values of threats from the threat matrix to the column side of control matrix –Identify the controls and add them to the row side of the control matrix –Compute aggregate values of importance of different controls Matrix Based Approach Methodology

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 13 There needs to be a threshold for determining the correlations within the matrices. For each matrix, the thresholds can be different. This can be done in two ways: Qualitatively –determined relative to other correlations –e.g. asset1/vulnerability1 (L) is much lower than asset3/vulnerability3 (H) correlation. asset2/vulnerability2 correlation is in-between (M) Quantitatively –determined by setting limits –e.g. if no correlation (0), if lower than 10% correlation (L), if lower than 35% medium (M), if greater than 35% (H) Matrix Based Approach Determining L/M/H

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 14 Although the example provided gives 4 different levels (Not Relevant, Low, Medium, and High), organizations may choose to have more levels for finer grained evaluation. For example: –Not Relevant (0) –Very Low (1) –Low (2) –Medium-Low (3) –Medium (4) –Medium-High (5) –High (6) Matrix Based Approach Extension of L/M/H

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 15 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 (L/M/H) –Compute the sum of product of vulnerability & asset values; add to impact column Matrix Based Approach Assets and Vulnerabilities Scale Not Relevant - 0 Low – 1 Medium – 3 High – 9 Critical Infrastructure Trade Secrets (IP)Client SecretsReputation (Trust)Lost Sales/RevenueCleanup CostsInfo/ IntegrityHardwareSoftwareServices Web Servers Compute Servers Firewalls Routers Client Nodes Databases Value Vulnerabilities Assets & Costs Relative Impact

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 16 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 Matrix Based Approach Vulnerabilities and Threats Scale Not Relevant - 0 Low– 1 Medium – 3 High – 9 Web Servers Compute ServersFirewallsRoutersClient NodesDatabases………… Denial of Service Spoofing and Masquerading Malicious Code Human Errors Insider Attacks Intrusion … Value Threats Vulnerabilities Relative ThreatImportance

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 17 Customize matrix based on the specific case –Add values from the relative exposure column of the previous matrix –Determine impact of different controls on different threats –Compute the aggregate value of benefit of each control Matrix Based Approach Threats and Controls Scale Not Relevant - 0 Low – 1 Medium – 3 High – 9 Denial of Service SpoofingMalicious CodeHuman ErrorsInsider AttacksIntrusionSpamPhysical Damage…… Firewalls IDS Single Sign-On DMZ Training Security Policy Value Controls Threats Value of Control Network Configuration Hardening of Environment

Sanjay Goel, School of Business/Center for Information Forensics and Assurance University at Albany Proprietary Information 18 Many methodologies are available for qualitative risk analysis. A matrix-based methodology incorporates a model which allows for aggregation of risks. This approach: –Brings transparency to risk analysis process –Provides a comprehensive methodology –Easy to use –Allows organizations to work with partial data –More data can be added as made available –Risk posture can be compared to other organization's –Determines controls needed to improve security Matrix-Based Approach Summary