System Assurance

Foreword

Preface

Chapter 1: Why hackers know more about our systems

1.1 Operating in cyberspace involves risks

1.2 Why hackers are repeatedly successful

1.3 What are the challenges in defending cybersystems?

1.3.1 Difficulties in understanding and assessing risks

1.3.2 Complex supply chains

1.3.3 Complex system integrations

1.3.4 Limitations of system assessment practices

1.3.5 Limitations of white-box vulnerability detection

1.3.6 Limitations of black-box vulnerability detection

1.4 Where do we go from here?

1.4.1 Systematic and repeatable defense at affordable cost

1.4.2 The OMG software assurance ecosystem

1.4.3 Linguistic modeling to manage the common vocabulary

1.5 Who should read this book?

Chapter 2: Confidence as a product

2.1 Are you confident that there is no black cat in the dark room?

2.2 The nature of assurance

2.2.1 Engineering, risk, and assurance

2.2.2 Assurance case

2.3 Overview of the assurance process

2.3.1 Producing confidence

2.3.2 Economics of confidence

Chapter 3: How to build confidence

3.1 Assurance in the system life cycle

3.2 Activities of system assurance process

3.2.1 Project definition

3.2.2 Project preparation

3.2.3 Assurance argument development

3.2.4 Architecture security analysis

3.2.5 Evidence analysis

3.2.6 Assurance case delivery

Chapter 4: Knowledge of system as an element of cybersecurity argument

4.1 What is system?

4.2 Boundaries of the system

4.3 Resolution of the system description

4.4 Conceptual commitment for system descriptions

4.5 System architecture

4.6 Example of an architecture framework

4.7 Elements of system

4.8 System knowledge involves multiple viewpoints

4.9 Concept of operations (CONOP)

4.10 Network configuration

4.11 System life cycle and assurance

4.11.1 System life cycle stages

4.11.2 Enabling systems

4.11.3 Supply chain

4.11.4 System life cycle processes

4.11.5 The implications to the common vocabulary and the integrated system model

Chapter 5: Knowledge of risk as an element of cybersecurity argument

5.1 Introduction

5.2 Basic cybersecurity elements

5.2.1 Assets

5.2.2 Impact

5.2.3 Threats

5.2.4 Safeguards

5.2.5 Vulnerabillities

5.2.6 Risks

5.3 Common vocabulary for threat identification

5.3.1 Defining discernable vocabulary for Assets

5.3.2 Threats and hazards

5.3.3 Defining discernable vocabulary for injury and impact

5.3.4 Defining discernable vocabulary for threats

5.3.5 Threat scenarios and attacks

5.3.6 Defining discernable vocabulary for vulnerabilities

5.3.7 Defining discernable vocabulary for safeguards

5.3.8 Risk

5.4 Systematic threat identification

5.5 Assurance strategies

5.5.1 Injury argument

5.5.2 Entry point argument

5.5.3 Threat argument

5.5.4 Vulnerability argument

5.5.5 Security requirement argument

5.6 Assurance of the threat identification

Chapter 6: Knowledge of vulnerabilities as an element of cybersecurity argument

6.1 Vulnerability as a unit of knowledge

6.1.1 What is vulnerability?

6.1.2 The history of vulnerability as a unit of knowledge

6.1.3 Vulnerabilities and the phases of the system life cycle

6.1.4 Enumeration of vulnerabilities as a Knowledge product

6.2 Vulnerability databases

6.2.1 US-CERT

6.2.2 Open source vulnerability database

6.3 Vulnerability life cycle

6.4 NIST Security content automation protocol (SCAP) ecosystem

6.4.1 Overview of SCAP ecosystem

6.4.2 Information exchanges in SCAP ecosystem

Chapter 7: Vulnerability patterns as a new assurance content

7.1 Beyond current SCAP ecosystem

7.2 Vendor-neutral vulnerability patterns

7.3 Software fault patterns

7.3.1 Safeguard clusters and corresponding SFPs

7.3.2 Direct injury clusters and corresponding SFPs

7.4 Example software fault pattern

Chapter 8: OMG software assurance ecosystem

8.1 Introduction

8.2 OMG assurance ecosystem: toward collaborative cybersecurity

Chapter 9: Common fact model for assurance content

9.1 Assurance content

9.2 The objectives

9.3 Design criteria for information exchange protocols

9.4 Trade-offs

9.5 Information exchange protocols

9.6 The nuts and bolts of fact models

9.6.1 Objects

9.6.2 Noun concepts

9.6.3 Facts about existence of objects

9.6.4 Individual concepts

9.6.5 Relations between concepts

9.6.6 Verb concepts

9.6.7 Characteristics

9.6.8 Situational concepts

9.6.9 Viewpoints and views

9.6.10 Information exchanges and assurance

9.6.11 Fact-oriented integration

9.6.12 Automatic derivation of facts

9.7 The representation of facts

9.7.1 Representing facts in XML

9.7.2 Representing facts and schemes in Prolog

9.8 The common schema

9.9 System assurance facts

Chapter 10: Linguistic models

10.1 Fact models and linguistic models

10.2 Background

10.3 Overview of SBVR

10.4 How to use SBVR

10.4.1 Simple vocabulary

10.4.2 Vocabulary entries

10.4.3 Statements

10.4.4 Statements as formal definitions of new concepts

10.5 SBVR vocabulary for describing elementary meanings

10.6 SBVR vocabulary for describing representations

10.7 SBVR vocabulary for describing extensions

10.8 Reference schemes

10.9 SBVR semantic formulations

10.9.1 Defining new terms and facts types using SBVR

Chapter 11: Standard protocol for exchanging system facts

11.1 Background

11.2 Organization of the KDM vocabulary

11.2.1 Infrastructure layer

11.2.2 Program elements layer

11.2.3 Resource layer

11.2.4 Abstractions layer

11.3 The process of discovering system facts

11.4 Discovering the baseline system facts

11.4.1 Inventory views

11.4.2 Build views

11.4.3 Data views

11.4.4 UI views

11.4.5 Code views

11.4.6 Platform views

11.4.7 Event views

11.5 Performing architecture analysis

11.5.1 Structure views

11.5.2 Conceptual views

Chapter 12: Case study

12.1 Introduction

12.2 Background

12.3 Concepts of operations

12.3.1 Executive summary

12.3.2 Purpose

12.3.3 Locations

12.3.4 Operational authority

12.3.5 System architecture

12.4 Business vocabulary and security policy for Clicks2Bricks in SBVR

12.5 Building the integrated system model

12.5.1 Building the baseline system model

12.5.2 Enhancing the baseline model with the system architecture facts

12.6 Mapping cybersecurity facts to system facts

12.7 Assurance case

Index

System Assurance teaches students how to use Object Management Group’s (OMG) expertise and unique standards to obtain accurate knowledge about existing software and compose objective metrics for system assurance.

OMG’s Assurance Ecosystem provides a common framework for discovering, integrating, analyzing, and distributing facts about existing enterprise software. Its foundation is the standard protocol for exchanging system facts, defined as the OMG Knowledge Discovery Metamodel (KDM). In addition, the Semantics of Business Vocabularies and Business Rules (SBVR) defines a standard protocol for exchanging security policy rules and assurance patterns. Using these standards together, students will learn how to leverage the knowledge of the cybersecurity community and bring automation to protect systems.

This book includes an overview of OMG Software Assurance Ecosystem protocols that integrate risk, architecture, and code analysis guided by the assurance argument. A case study illustrates the steps of the System Assurance Methodology using automated tools.

This book is recommended for technologists from a broad range of software companies and related industries; security analysts, computer systems analysts, computer software engineers-systems software, computer software engineers- applications, computer and information systems managers, network systems and data communication analysts.

• Provides end-to-end methodology for systematic, repeatable, and affordable System Assurance.
• Includes an overview of OMG Software Assurance Ecosystem protocols that integrate risk, architecture and code analysis guided by the assurance argument.
• Case Study illustrating the steps of the System Assurance Methodology using automated tools.

Technologists from a broad range of software companies and related industries; Security Analysts; Computer Systems Analysts, Computer Software Engineers-Systems Software, Computer Software Engineers- Applications, Computer and Information Systems Managers, Network systems and Data Communication Analysts.