DEVELOPMENT OF METAMODEL FOR INFORMATION SECURITY RISK
MANAGEMENT
MOHAMMED SALEM MOHAMMED BA MUQABEL
A project submitted in partial fulfillment of the
requirements for the award of the degree of
Master of Computer Science (Information Security)
Faculty of Computing
Universiti Teknologi Malaysia
JUNE 2013
iii
“Dedicated to my beloved family without their understanding, supports, and
most of all love, the completion of this work would not have been possible.”
iv
ACKNOWLEDGEMENT
I would like to express my gratitude to Allah for providing me the blessing to
complete this work. Hence, I am deeply grateful to my supportive and helpful
supervisor Dr Siti Hajar Binti Othman for assisting and guiding me in the
completion of this project. With all truthfulness, without Allah then her support and
motivate, this project would not have been a complete. Dr Siti Hajar Binti Othman
has always been my source of motivation and guidance. For that, I am truly grateful
for her continual support and cooperation in assisting me all the way through my
master.
To my family, no words can describe my gratefulness for always being
there despite of the distance. They have showered me with love and compassion and
enrich my life like no other. They are the source of comfort and kept me focus the
priorities in life and therefore, this work is dedicated to them.
My thanks also extend to my friends, for their enlightening companionship
and encouragement of trudging through all the moments from down to up the hill in
the run to complete Master Project. I would not have done it without the help and
motivation from all of them.
v
ABSTRACT
Nowadays, information technology and information system have been used
widely in many fields such as in business, education, marketing, transportation,
medical and many other fields. In information technology and system field, a
security aspect plays a vital role and thus become a challenging issue. Thus security
should be ready installed and resistance to various numbers of potential attacks. In
Information Security and Information Technology, it is important to decide what
countermeasures that could potentially harm the organization from achieving their
business objectives. Reducing risk to an acceptable level is among the main target of
the risk management process. On other hand, the main reasons to fail in Information
Security Risk Management (ISRM) is the complexity and inflexibility of the existing
models. Domain modulars usually spend a lot of times to understand the nature of the
domain which they desire to model. Even though there are many existing ISRM
models appears, but to find a suit model which could provide a straight guideline to
the ISRM users based on their own problems are limited. To solve this issue, this
project follows seven steps to create a generic metamodel which can describe the
semantics of ISRM models and its solutions through one unified model. Then
validates ISRM by three validation techniques; Frequency-based Selection, Face
validity and Tracing technique. Through the metamodel various risk management
problems faced by different levels of ISRM users can be solved based on the
problem attributes such as, risk determination specific to a firewall vulnerability
problems, risk assessment for an information security project management. Directly,
this can help many users/newcomers to this domain to easy understand the concepts
required for their own information security risk problem.
vi
ABSTRAK
Pada masa kini, teknologi maklumat dan sistem maklumat yang telah digunakan
secara meluas dalam pelbagai bidang seperti perniagaan, pendidikan, pemasaran,
pengangkutan, perubatan dan pelbagai bidang lain. Dalam teknologi dan sistem
maklumat bidang, aspek keselamatan memainkan peranan yang penting dan dengan
itu menjadi satu isu yang mencabar. Oleh itu, keselamatan harus dipasang siap dan
penentangan terhadap pelbagai nombor serangan yang berpotensi. Dalam
Keselamatan dan Maklumat Teknologi Maklumat, adalah penting untuk menentukan
apa langkah-langkah tindakan yang berpotensi boleh merosakkan organisasi daripada
mencapai objektif perniagaan mereka. Mengurangkan risiko kepada tahap yang
boleh diterima adalah antara sasaran utama proses pengurusan risiko. Pada tangan
yang lain, sebab-sebab utama untuk gagal dalam Maklumat Pengurusan Risiko
Keselamatan (ISRM) adalah kompleks dan tidak fleksibel daripada model yang sedia
ada. Modulars Domain biasanya menghabiskan banyak masa untuk memahami sifat
domain yang mereka inginkan untuk model. Walaupun terdapat banyak model yang
sedia ada ISRM muncul, tetapi untuk mencari model yang sesuai yang boleh
memberikan satu garis panduan lurus kepada pengguna ISRM berdasarkan masalah
mereka sendiri adalah terhad. Untuk menyelesaikan isu ini, projek ini mengikuti
tujuh langkah untuk mewujudkan metamodel generik yang boleh menggambarkan
semantik model ISRM dan penyelesaian melalui satu model bersatu. Kemudian
mengesahkan ISRM oleh tiga teknik pengesahan; Pemilihan berasaskan Frekuensi,
kesahan muka dan menilik teknik. Melalui metamodel pelbagai masalah pengurusan
risiko yang dihadapi oleh tahap yang berbeza dari pengguna ISRM boleh
diselesaikan berdasarkan masalah ciri-ciri seperti, penentuan risiko khusus kepada
masalah kelemahan firewall, penilaian risiko untuk pengurusan projek keselamatan
maklumat. Secara langsung, ini boleh membantu ramai pengguna / pendatang baru
kepada domain ini untuk mudah memahami konsep yang diperlukan untuk masalah
risiko keselamatan maklumat mereka sendiri.
vii
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
A CKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xi
LIST OF FIGURES xii
LIST OF APPENDIXES xv
1 RESEARCH OVERVIEW
1.1 Introduction 1
1.2 Problem Background 2
1.3 Problem Statement 4
1.4 Research Questions 5
1.5 Objectives of Project 5
1.6 Scope of the Project 6
1.7 Project Structure 6
2 LITERATURE REVIEW
2.1 Introduction 8
viii
2.2 Risk Review 8
2.2.1 Risk Management 11
2.2.2 Key Roles of Risk Management 13
2.3 Information Security 15
2.3.1 Information Security Overview 15
2.3.2 Characteristics of Information Security 16
2.3.3 Different between information privacy and
information security 17
2.4 Model-Driven Software Engineering 18
2.4.1 Model 18
2.4.2 Model-driven Software Development 20
2.5 Metamodels, Ontologies and Metamodelling 22
2.5.1 Metamodelling Frameworks 23
2.5.2 MOF metamodelling and metamodelling
processes 25
2.5.3 Metamodel Benefits 27
2.5.4 Metamodel Development 28
2.5.5 Metamodel Validation 31
2.5.6 Metamodel Maintenance 34
2.7 Summary 35
3 RESEARCH METHODOLOGY
3.1 Introduction 36
3.2 Design Science Research and its Justification for this
Research 36
3.3 Operational Framework 39
3.2.1 Phase 1 – Research Problem Definition 39
3.2.2 Phase 2 – ISRM Metamodel Creation 39
3.2.3 Phase 3 – ISRM Metamodel Validation 40
3.2.4 Phase 4 – ISRM Metanodel Result 42
3.4 Construct ISRM Metamodel 43
3.5 Summary 44
4 INITIAL DEVELOPMENT OF INFORMATION
SECURITY RISK MANAGEMENT METAMODEL
ix
4.1 Introduction 45
4.2 Metamodelling Process towards a ISRMM 46
4.2.1 Step 0: Models collection and preliminary
domain study 47
4.2.2 Step 1: Identifying sets of model 47
4.2.3 Step 2: Extraction of general concepts 49
4.2.4 Step 3: Short-listing candidate definitions 50
4.2.5 Step 4: Reconciliation of Candidate
Concept Definitions 51
4.2.6 Step 5: Designation of concepts 52
4.2.7 Step 6: Identification of relationships 54
4.2.8 Step 7: Validation the Metamodel 57
4.3 The Result of Initial Metamodel (ISRMM Version 1.0)
57
4.3.1 ISRMM 1.0: Risk Identification- phase class
of concepts 58
4.3.2 ISRMM 1.0: Risk Assessment and
Evaluation- phase class of concepts 59
4.3.3 ISRMM 1.0: Risk Mitigation- phase class of
concepts 60
4.3.4 ISRMM 1.0: Risk Monitoring and Review-
phase class of concepts 61
4.4 Summary 62
5 VALIDATION OF INFORMATION SECURITY RISK
MANAGEMENT METAMODEL
5.1 Introduction 63
5.2 Validation 1: Frequency based Selection 64
5.2.1 Frequency-based Selection Result and
ISRMM updated version (ISRMM1.1) 65
5.3 Validation 2: Face Validity-Expert Interview and
Discussion 69
5.3.1 Expert Evaluation 70
5.3.2 Expert Evaluation Result 71
5.4 Validation 3 : Tracing 72
5.5 Summary 83
x
6 CONCLUSION AND RECOMMENDATION
6.1 Introduction 84
6.2 Project Achievement 84
6.3 Project Constrains 86
6.4 Future Work 87
6.5 Summary
88
REFERENCES 89
Appendix A-E 95-109
xi
LIST OF TABLES
TABLE NO TITLE PAGE
2.1 Key Roles of Risk Management 13
2.2 Existing metamodelling frameworks. 24
2.3 Diverse metamodels applications 28
2.4 Metamodel Development Process 29
2.5 Metamodel Validation Techniques 33
3.1 Metmodel Development Steps Activities 42
4.1 A set of 10 ISRM models for development (Set I) 48
4.2 Sample of extraction concepts from NIST Framework 49
4.3 Sample of extraction concepts from ISO/IEC 27005-2008
Standard 50
4.4 Concepts reconciled in Step 4 are designated into four
ISRM-phase 53
4.5 Samples of relationships among concepts in ISRMM 54
4.6 Initially Identified Risk Identification- phase Concepts and
their Definitions 57
4.7 Initially Identified Risk Assessment & Evaluation - phase
concepts and their definitions 59
4.8 Initially Identified Risk Mitigation - phase Concepts and
their Definitions 61
4.9 Initially Identified Risk Monitoring and Review - phase
Concepts and their Definitions 62
5.1 DoC categories 65
5.2 Frequency of Risk Identification Concepts 65
5.3 Frequency of Risk Assessment and Evaluation Concepts 66
5.4 Frequency of Risk Mitigation Concepts 66
5.5 Frequency of Risk Monitoring and Review Concepts 66
5.6 DOC classification for ISRMM concepts 67
xii
5.7 Validation personal 70
5.8 All the phase classes of concepts through all the validation
techniques 77
5.9 Final Version of ISMS 1.2 81
xiii
LIST OF FIGURES
FIGURE NO TITLE PAGE
2.1 Total number of internet vulnerabilities identified,
2006-2011 10
2.2 Number of vulnerabilities Month-by-month
2010&2011 11
2.3 Information Security Characteristics 16
2.4 Relating real world, model and metamodel
elements 21
2.5 MOF framework 25
2.6 The evolution of metamodel, model and
requirement 34
3.1 Research Framework 38
3.2 A framework of the ISRM Creation Process 41
4.1 Creating a relationship between Asset and Value
concepts 56
4.2 The ISRMM is represented in 4-phase continuous
classes of concept 56
4.3 ISRMM 1.0: Risk Identification- phase class
concepts 58
4.4 ISRMM 1.0: Risk Assessment & Evaluation -
phase class concepts 59
4.5 ISRMM 1.0: Risk Mitigation - phase class
concepts 60
4.6 ISRMM 1.0: Risk Monitoring and Review - phase
class of concepts 61
5.1 ISRMM1.1: A validated version of Risk
Identification -phase class of concepts 68
5.2 ISRMM1.1: A validated version of Risk
Mitigation-phase class of concepts 69
5.3 ISRMM1.2: A validated version of Risk
Mitigation-phase class of concepts 72
xiv
5.4 The Risk Identification Model (M1) derived from
ISRMM towards CICT, UTM 74
5.5 The Risk Assessment and Evaluation Model (M1)
derived from ISRMM towards CICT, UTM 74
5.6 The CICT Risk Identification model (Real World
model, M0), instigated from the Risk
Identification Model (Model, M1) 75
5.7 The CICT Risk Assessment and Evaluation model
(Real World model, M0), instigated from the Risk
Assessment and Evaluation Model (Model, M1) 75
5.8 All the phase classes of concepts through all the
validation techniques 77
5.9 Final Version of ISMS 1.2 81
xv
LIST OF APPENDIXES
APPENDIX TITLE PAGE
A A set of 10 ISRM models for development 95
B Extraction of general concepts 99
C Short-listing candidate definitions 101
D Set V for Frequency based Selection technique 106
E ISRMM Validation 109
1
CHAPTER 1
INTRODUCTION
1.1 Introduction
Nowadays, information technology and information system have been used
widely in many fields such as in business, education, marketing, transportation,
medical and many other fields. In information technology and system field, a
security aspect plays a vital role and thus become a challenging issue. Security is
required in this field as a ready-installed resistance to various numbers of potential
attacks. This pose a great challenge in ensuring the success of security embedded in
the information technology and system field. Risk management is an ongoing and
iterative process. In information technology and information security field, risk
management can be defined as the process of identifying vulnerabilities and threats
to the information resources used by an organization. To decide what
countermeasures that could potentially harm the organization from achieving their
business objectives is important. Reducing risk to an acceptable level is among the
main target of the risk management process.
The necessary of security technologies and technical programs has been
recognized on a large scale therefore receiving a continuous attention (for example,
new encryption algorithms, public key infrastructure, etc.). Return on Security
Investment (ROSI) issues, related to the costs spent on security technologies
compared with their benefits. Nevertheless, to protect an organization’s business
2
firstly need to identify and evaluate the relationship between the assets and their
security. The essential problem to align the information technology assets and
security is through a Risk Management (RM) process. Therefore, to handle the risk
of an unexpected events and threats happen to many information security assets and
personnel, it is believed that a metamodel could offer its benefit. The organization of
the knowledge complexity of this domain can be managed through the artifact called
the Information Security Risk Management (ISRM) metamodel. ISRM is a security
method that can manage and reduce threats and vulnerabilities and also can mitigate
security risks (Xuan, Wuwong et al. 2010). Over time, organizations tend to relax
their security posture. To combat a relaxation of security, organization should apply
a model to perform security risk management.
In the first chapter, the problem background of this research is explained.
This is then continued with a discussion on this research’s problem statement. Next,
it is followed by a discussion on research question, research objectives, research
scopes and a summary of this project structure.
1.2 Problem Background
The reason of failing to prevent disasters and threats on the subsequent
management are rarely caused by a single factor. They are often a result of the
accumulation of a complex series of events and often accompanied by changes in the
external environment factors (Aini, Fakhrul-Razi et al., 2005). It is a common
wisdom that there are no two risks exactly having the same impact, each one has its
own impact, and that every risk requires its own management process. For example,
risk mitigation is an ISRM action that is applicable in many risk situations.
Whenever organization faces a problem of risk, usually remembers previous accident
scenario involving with same key features with one at hand and apply the solution
that worked previously. By matching previously effective actions with the current
situation, an appropriate course of action is recalled and put into effect promptly
(Paton and Jackson, 2002). Therefore, this research aims to use a generic
3
representational layer (a metamodel) to give a unified view of common concepts and
actions that apply in several of information security risk management.
Complexity and inflexibility of the model for the domain user is an essential
reason to fail in many of ISRM. Domain modulars usually spend a lot of times to
understand the nature of the domain which they desire to model. Generally, most of
them use a general purpose language such as Unified Modeling Language UML in
modeling their domain application models. However, when the created models do
not perfectly fit their needs. Therefore, a more specific domain modeling language
such as ISRM is believed that it can offer a better alternative approach to the
problem.
The problem when designing a new model of the domain is the issue of
identification of the domain concepts and ambiguity of the concept terminologies.
This will be a big problem especially to the newcomers of the domain (in this case,
the newcomer is any person who new to the ISRM domain). As with any domain, the
power of its domain-specific language is directly tied to the abstraction level of the
domain concepts. Thus, the more semantic meaning attached to domain-concepts,
the less time the modeler spends specifying the domain models (Sprinkle, 2003). The
meaning and definition of specific concept terminologies and their relationships are
discipline specific and may differ from one observer to the other one (Gharehdaghli,
2003), so domain concepts can have multiple descriptions. The field of ISRM
requires a flexible structure to allow it offering a facility to store and retrieve not
only observed and measured data, but also interpretative and inferred information of
its domain. Thus through the creation of Information Security Risk Management
Metamodel (ISRMM) library the ambiguity problem of ISRM terminologies could
be solved.
There are a lot of models are available in for the information security analysis
and end user from various fields; it might look as an advantage for them. However,
this statement might be true only if the user requirements are the same and well
defined. But not, in the wide area such a security risk management that contains
4
many a huge number of different aspect and terminologies. In order to simplify this
activity, the proposed ISRMM is believed can describes all contained ISRM model
concepts and the way they are arranged, related and constrained.
These has motivated the researcher in aiming to provide all security risk
management users especially the ISRM model developers with a complete set of
reference model through the development of ISRMM. The metamodel can provide a
wide range of capability encompassing serving to different kinds of security risk in
Information Security such as, risk in network or cloud computing, risk in internal or
external InfoSec project management, risk in access control process. With various
level of ISRM users such as, security manager, information security officers,
network technical personnel, IT managers in business, hospital, university and etc.
1.3 Problem Statement
Normally, when people face a problem, they approach facilitates recalling
prior incident scenarios that share key features with the one at hand and apply the
solution that worked previously. Knowledge of ISRM is huge and scattered
everywhere. It is believed that many problems faced by various information security
users actually have their own successful solutions. However, because of the solutions
are scattered in different places and also takes time to find it, therefore, it is difficult
for the current users ,who may be facing the same problem, to reuse the solution
based on their own security risk management problem. In information security field,
many organizations either in both public and private sectors suffer with the
complexity and inflexibility of the information security risk framework and models.
Even though there are many existing ISRM models that appears, but to find a suiting
model which could provide a straight guideline to the ISRM users based on their
own problems are limited. To solve this issue, this project creates a metamodel
which can describe the semantics of ISRM models and its solutions through one
unified model. For metamodel, the concepts created in the artifact will come together
with the repository (domain solution) of the concept. It is believed, through the
5
metamodel various risk management problems faced by different levels of ISRM
users can be solved based on the problem attributes such as risk determination
specific to a firewall vulnerability problems, risk assessment for information security
project management. Directly, this can help many users/newcomers to this domain to
easily understand the concepts required for their own information security risk
problem.
1.4 Research Questions
In order to address the issues highlighted in the previous section, there are
two research questions that need to be tackled:
1. How to create a generic ISRM metamodel through the observation against
existing domain model?
2. How the proposed ISRM metamodel can provide such a modeling guidelines
for various ISRM users in solving their own risk problem?
1.5 Objective of Project
This research has three objectives:
i. To study and analyze how a metamodelling approach could capable to
support the complexity of knowledge in the ISRM domain thorough
investigation to various existing ISRM models from different sources.
ii. To develop the ISRM Metamodel (a specific modeling language for
the ISRM domain) by using the 7-Steps of a metamodel development.
iii. To validate the proposed ISRM metamodel by using the metamodel
validation techniques.
6
1.6 Scope of Project
The scope that identifies the boundaries of the project listed below:
i. The research will focus on the creation of a metamodel in level M2 (the
representation of ISRM metamodel) by using the Four-layer Meta Object
Facility (MOF) metamodelling framework. The creation will use a set of
10 existing ISRM models.
ii. For the purpose of metamodel validation, the proposed metamodel will
use three technique of metamodel validation known as the ‘Frequency
based Selection’, ‘Face Validity’, and ‘Tracing’. For ‘Frequency based
Selection’ a set of 3 models (Set V) will be used.
iii. For the purpose of showing the applicable of the metamodel in real world
ISRM domain, an instantiation of models from the ISRM metamodel will
be presented by using one ISRM case study problem.
1.7 Project Structure
This project is structured into six chapters. To facilitate access to the project,
a brief description of the contents of each chapter. Chapter 1: Gives an introduction
to the research and guides the reader through a brief description of the research area
(problem background, problem statement, research objectives and research scopes).
Chapter 2: Provides a review result on the relevant published research work. It also
includes the processes and techniques used to create the ISRM metamodel. Chapter
3: Describes a research methodology conducted for this project, where a design
science research methodology is used. In here, this project justifies the use of
methodology and describes the four phases of the research used in this project:
ISRMM problems identification, ISRM metamodel creation and ISRM validation.
Chapter 4: Performs the eight steps ISRMM creation process and presents the initial
result of the ISRM Metamodel. For this purpose, a set of 10 existing ISRM Models
are used.
7
Chapter 5: Validates the initial version of ISRMM (version 1.0) by applying
three validation techniques, Frequency-based Selection, Face Validity and Tracing
technique. Chapter 6: Summarizes the research findings, draws conclusions, and
outlines future works and possibilities for extending this research.
89
REFERENCES
Aini, M. S., et al. (2005). "Analysis Of Royal Inquiry Report On The Collapse Of A
Building In Kuala Lumpur: Implications For Developing Countries." Disaster
Prevention and Management 14(1): 55-79.
Alhir (2003) Understanding the Model Driven Architecture (MDA).
Andreasen, M., et al. (2002). Design Typology and Design Organisation.
Author (2012). Characteristics of Information Security.
Beers, W. (2005). Kriging Metamodelling For Simulation, Tilburg University. PhD
Thesis.
Belle, V. (2004). Framework for Evaluation of Enterprise Model. Cape Town,
University of Cape Town. PhD
Beydoun, G., et al. (2009). "FAML: A Generic Metamodel for MAS Development."
IEEE Transactions on Software Engineering 35(6): 841-863.
Bonnette, C. A. (2012) Assessing Threats To Information Security In Financial
Institutions
Cook, S. (2004). "Domain-Specific Modeling and Model Driven Architecture." MDA
Cross, N. (2007). Editorial: Forty years of design research. D. Studies.
Garcia, P. B. (2007). A Metamodel To Annotate Knowledge Based Engineering
Codes As Enterprise Knowledge Resources, Cranfield University. PhD 489.
Gargantini, A., et al. (2009). "A semantic framework for metamodel-based
languages." Automated Software Engineering 16(3): 415-454.
Geotgetown_University (2013). Georgetown University’s Risk Management Process
Gharehdaghli, A. (2003). Design of a Generic Metamodel for Fieldwork Data
Management. International Institute For Geo=Information, Science and Earth
Observation Enschede, Netherlands. Master of Science in Geoinformatics.
91
Goeken, M. and S. Alter (2009). Towards Conceptual Metamodeling of IT
Governance Frameworks Approach - Use - Benefits. Hawaii International
Conference on System Sciences.
Herold, R. (2002). "What is the Difference Between Security and Privacy." CSI.
Hevener, A., et al. (2004). Design Science in Information Systems Research. MIS
Quarterly.
Hevner, A. and S. Chatterjee (2010). "Design Science Research in Information
Systems." Springer US.
Hevner, A., et al. (2004). Design Science in Information Systems Research. MIS
Quarterly.
Institute, S. (2003). Global Information Assurance Certification Paper. Information
Risk Assessment.
ISO/IEC:27001, I. O. f. S. (2005). ISO/IEC 27001. Information technology - Security
techniques - Information security management systems Requirements.
Geneva.
ISO/IEC:27005, I. O. f. S. (2008). ISO/IEC 27005. Information technology - Security
techniques - Information security risk management. Geneva.
ISO/IEC_Guide73, I. O. f. S. (2002). ISO/IEC Guide 73. . Risk management -
Vocabulary - Guidelines for use in standards. Geneva.
ISO:3100, I. O. f. S. A. N. (2009). AS/NZS ISO 3100. Risk management - Principles
and guidelines. Australia, Standards Australia/ Standards New Zealand.
Jalali, V. and M. Matash Borujerdi (2011). "Information retrieval with concept-based
pseudo-relevance feedback in MEDLINE." Knowledge and Information
Systems 29(1): 237-248.
Jonathan, S., et al. (2007). Metamodelling: state of the art and research challenges. he
International Dagstuhl conference on Model-based engineering of embedded
real-time systems. Dagstuhl Castle, Germany, Springer.
Jones, J. (2005). An Introduction to Factor Analysis of Information Risk (FAIR).
Kassab, M., et al. (2009). A Metamodel for Tracing Non-functional Requirements.
Computer Science and Information Engineering, 2009 WRI World Congress
on.
Kelly, C. (1998). Simplifying Disasters: Developing a Model For Complex Non-
Linear Events Disaster Management: Crisis and Opportunity: Hazard
91
Management and Disaster Preparedness in Australasia and the Pacific Region
Conference. Cairns, Queensland.
Kelly, S. and R. Pohjonen (2009). "Worst Practices for Domain-Specific Modeling."
Software, IEEE 26(4): 22-29.
Kelly, S. and R. Pohjonen (2009). Worst Practices for Domain-Specific Modeling.
IEEE Software.
Khoo, Y., et al. (2001). "Motivation for ISO 14000 certification: development of a
predictive model." Omega.
Kleijnen, J. P. C. and R. G. Sargent (2000). "A Methodology for Fitting and
Validating Metamodels in Simulation." European Journal of Operational
Research 120: 14-29.
Kok, et al. (2010). Feature selection for fluency ranking. Proceedings of the 6th
International Natural Language Generation Conference. Trim, Co. Meath,
Ireland, Association for Computational Linguistics: 155-163.
Lagerstrom, R., et al. (2010). "Architecture analysis of enterprise systems
modifiability - Models, analysis, and validation." Journal of Systems and
Software.
Levendovszky , T., et al. (2010). "Model Evolution and Management, MBEERTS."
Springer.
Lhoste, P. and M. Lomabard (2008). "Information modelling framework for
knowledge emergence in product design."
M. Picka (2004). "Metamodelling and Development of Information System."
Agriculture Economics 2(50): 65-70.
Manning, C. D., Raghavan, Prabhakar, Hinrich (2008). Introduction to Information
Retrieval. Cambridge, UK, Cambridge University Press.
March, S. and G. Smith (1995). "Design and natural science research on information
technology."
Mayer, N., et al. (2009). "Design of a Modelling Language for Information System
Security Risk Management ".
MYLOPOULOS, j. (1992). Conceptual modeling and Telos, Conceptual modeling,
Databases and Case:An Integrated View of Information Systems
Development, Wiley.
Nordstrom (1999). etamodeling - Rapid Design and Evolution of Domain-Specific
Modeling Environments, Vanderbilt University. Ph.D Theses.
92
OMG (2001). Unified Modelling Language Specification, Object Management Group.
OMG (2002). Meta Object Facility (MOF) Specification, Object Management Group.
OMG (2003). MDA Guide Version 1.0.1.
OMG (2011). Unified Modeling Language (UML).
Othman, S. and G. Beydoun (2010). Metamodelling Approach To Support Disaster
Management Knowledge Sharing. Australasian Conference on Information
Systems (ACIS'2010)
Paton, D. and D. Jackson (2002). "Developing Disaster Management Capability: An
Assessment Centre Approach." Disaster Prevention and Management 11(2):
115-122.
Pidd, M. (2000). ools for Thinking - Modeling in Management Science. New York,
Wiley.
Pigott, D. and V. Hobbs (2011). Complex knowledge modelling with functional entity
relationship diagrams.
Roberts, J. and K. Gary (2008) The industry responds on privacy. Modern Healthcare
Rossi , M., et al. (2004). "Managing Evolutionary Method Engineering by Method
Rationale." Journal of the Association for Information Systems.
Saleh, B. and F. Masseglia (2011). "Discovering frequent behaviors: time is an
essential element of the context." Knowledge and Information Systems 28(2):
311-331.
Sargent, R. G. (2005). Verification and Validation of Simulation Models. Proceedings
of the 37th Conference on Winter Simulation. Orlando, Florida, Winter
Simulation Conference.
Scheer (2005). "Process Modeling using Event-Driven Process Chains." Wiley.
Seidewitz (2003). "What Models Mean." IEEE Software.
SEMA (2005). Samhällets Informationssäkerhet. Swedish Emergency Management
Agency. Swedish, Stockholm.
Simon, H. (1996). The Sciences of Artificial, Cambridge.
Sowa, J. F. (1984). Conceptual structures: information processing in mind and
machine, Addison-Wesley Longman Publishing Co., Inc.
Sprinkle, J. M. (2003). Metamodel Driven Model Migration. Tennessee, US,
Vanderbilt University. Doctor of Philosophy: 176
93
Stoneburner, G., et al. (2002). NIST Special Publication 800-30. Risk Management
Guide for Information Technology Systems. Gaithersburg, National Institute
of Standards and Technology.
Sturm, A. (2009). How to Choose a Metamodeling Approach. The Knowledge
Industry Survival Strategy. Initiative workshop of The International
Conference on Object Oriented Programming, systems, Languages and
Applications (OOPSLA) Orlando. Florida USA.
Symantic (2011). Information Security yearly report
Symantic (2012). Total number of internet vulnerabilities identified, 2006-2011
Symentec.cloud (2012). Number of vulnerabilities Month-by-month 2010&2011.
Symentec.cloud (2012). Total number of internet vulnerabilities identified from 2006-
2011 symentec, Symentec
Thomas, K., et al. (2003). "Model-Driven Development: A Metamodeling
Foundation." IEEE.
Trabelsi, C., et al. (2011). "A Model-Driven Approach for Hybrid Power Estimation
in
Embedded Systems Design." EURASIP Journal on Embedded Systems.
Vahidov, R. (2006). Proceedings of the First International Conference on Design
Science Research in Information Systems and Technology
Voelter, M., et al. (2005). Model-Driven Software Engineering, Technology,
Engineering, Management, John-Wiley & Sons Ltd.
Wachsmuth, G. (2007). Metamodel Adaptation and Model Co-adaptation.
Proceedings of 21st European Conference on Object-Oriented Programming
(ECOOP). Germany, Springer-Verlag: 24.
Wallin, E. and Y. Xu (2008). Managing Information Security in Healthcare. School of
Economics and Managment, Lund University: 73.
Weilkiens, T., Ed. (2008). Systems Engineering with SysML/UML, Morgan
Kaufmann Publishers Inc.
Xuan, Z., et al. (2010). Information Security Risk Management Framework for the
Cloud Computing Environments. Computer and Information Technology
(CIT), 2010 IEEE 10th International Conference on.
Yan, X. and C. Lin (2010). Term-frequency Based Feature Selection Methods for
Text Categorization. Genetic and Evolutionary Computing (ICGEC), 2010
Fourth International Conference on.
94
Zhang, Z. and N. Ye (2011). "Locality preserving multimodal discriminative learning
for supervised feature selection." Knowledge and Information Systems 27(3):
473-490.
Zschaler, S., et al. (2006). Ontologies, Meta-models, and the Model-Driven Paradigm,
Springer Berlin Heidelberg.
