Capturing, Organizing, and Reusing Knowledge of NFRs: An NFR Pattern Approach Sam Supakkul 1 Tom Hill 2 Ebenezer Akin Oladimeji 3 Lawrence Chung 1 1 The University of Texas at Dallas 2 EDS, an HP company 3 Verizon Communications

Security = “bad things to be prevented” * * C. Haley and B. Nuseibeh, IEEE TSE, 2008 To prevent such incident, we need to know: Meaning of credit card security? Problems suffered by TJX? Root causes of those problems? Mitigation alternatives of the problems and their causes? Choosing and developing the mitigations with consideration of other organizational needs? The TJX incident, the largest credit card theft in history

Difficult to get technical details from case reports The TJX case attack scenario Developed after: reading over 30 articles studying computer security educated assumptions Problem: Lack of security knowledge

Problem: Difficult to possess necessary NFRs related knowledge

A solution: Applying NFRs knowledge captured as patterns

Goal Pattern Name: FISMA Security Objectives Objective: refine Security Domain: Model: Known uses: FISMA, US military Goal pattern captures a definition of an NFR

Problem pattern Name: TJX Security Problems Domain: Objective: break Privacy[Payment card info] Model: Experiences: TJX Problem pattern captures an undesirable situation that can hurt an NFR

Causal Attribution Pattern Name: Unauthorized Server Access Causes Domain: Objective: make Unauthorized Access [Server] Model: Experiences: TJX Causal Attribution pattern captures causes and root causes of a problem

Problem classification Undesirable situation Undesirable operation Vulnerability

Problem mitigation classification Undesirable situation Undesirable operation Vulnerability Change environment to that with more acceptable risks Prevent the operation from being realized Prevent the operation from causing the undesirable situation Prevent/limit the effect on the goal

Solution Alternatives Pattern Name: Unauthorized Server Access Mitigation Domain: Objective: hurt Unauthorized access [server] Model: Experiences: Name: Masquerading User Login Mitigation Domain: Objective: break Masquerading user login Model: Experiences: Name: Clear text ID/password Mitigation Domain: Objective: break Clear text ID/password Mitigation Model: Experiences:

Alternatives Selection Pattern Name: Usability Driven Unauthorized Server Access Mitigation Domain: Objective: select Unauthorized Server Access Mitigation, Masquerading User Login Mitigation, Clear Text ID/Password Mitigation Model: Experiences: select

Result of a selection pattern project Selection Pattern Goal PatternProblem PatternCasual Pattern Alternatives Patterns

Requirements Pattern What are requirements?

Requirements Assumption Requirements Goals assignable to agents in the software-to-be [van Lamsweerde, ICSE00] Requirements “requirements that indicate what the customer needs from the system, described in terms of its effect on the environment” [Gunter, Gunter, Jackson, Zave, IEEE Software 2000] World RequirementSpecificationProgram Machine RequirementsSpecifications [R. Seater, D. Jackson, IWAAPF’06] Problem Frames

Requirements Pattern Name: Strong password requirements Domain: Objective: make Non-dictionary password, Frequently changed password Model: Experiences:

Pattern organization

Pattern specialization Properties Specialization of context/topic More restrictive content

Pattern aggregation Manual application of multiple patterns -Know which patterns to use -Know which order to apply -But flexible Pre-assembled patterns into an aggregate pattern -Ready-to-use -More cohesive knowledge -Narrower applicability

Pattern classification/meta-pattern [Supakkul, Hill, Oladimeji, Chung, PLoP09]

Pattern operations Search operation Apply operation Examples of the apply operation

Conclusion Contributions –Capturing and reusing different kinds of NFR knowledge using patterns –Organization of patterns along the 3 dim. Future work –More precise definition of the concepts –Tool support to verify the concepts –More case studies to validate the general applicability for other NFRs

Capturing, Organizing, and Reusing Knowledge of NFRs: An NFR Pattern Approach Sam Supakkul 1 Tom Hill 2 Ebenezer Akin Oladimeji 3 Lawrence Chung 1 1 The University of Texas at Dallas 2 EDS, an HP company 3 Verizon Communications