Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals
Antonio SANTOS-OLMO1,2(), Luis Enrique SÁNCHEZ1,2, David G. ROSADO1, Manuel A. SERRANO3, Carlos BLANCO4, Haralambos MOURATIDIS2, Eduardo FERNÁNDEZ-MEDINA1
1. GSyA Research Group, University of Castilla-La Mancha, Ciudad Real 13071, Spain 2. Institute for Analytics and Data Science, University of Essex, Colchester CO4 3SQ, UK 3. Alarcos Research Group, University of Castilla-La Mancha, Ciudad Real 13071, Spain 4. ISTR Research group, Department of Computer Science and Electronics, University of Cantabria, Santander 39005, Spain
The information society depends increasingly on risk assessment and management systems as means to adequately protect its key information assets. The availability of these systems is now vital for the protection and evolution of companies. However, several factors have led to an increasing need for more accurate risk analysis approaches. These are: the speed at which technologies evolve, their global impact and the growing requirement for companies to collaborate. Risk analysis processes must consequently adapt to these new circumstances and new technological paradigms. The objective of this paper is, therefore, to present the results of an exhaustive analysis of the techniques and methods offered by the scientific community with the aim of identifying their main weaknesses and providing a new risk assessment and management process. This analysis was carried out using the systematic review protocol and found that these proposals do not fully meet these new needs. The paper also presents a summary of MARISMA, the risk analysis and management framework designed by our research group. The basis of our framework is the main existing risk standards and proposals, and it seeks to address the weaknesses found in these proposals. MARISMA is in a process of continuous improvement, as is being applied by customers in several European and American countries. It consists of a risk data management module, a methodology for its systematic application and a tool that automates the process.
. [J]. Frontiers of Computer Science, 2024, 18(3): 183808.
Antonio SANTOS-OLMO, Luis Enrique SÁNCHEZ, David G. ROSADO, Manuel A. SERRANO, Carlos BLANCO, Haralambos MOURATIDIS, Eduardo FERNÁNDEZ-MEDINA. Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals. Front. Comput. Sci., 2024, 18(3): 183808.
? Interrelationships between risks according to control areas
P2
Networks
Fuzzy techniques
? Risks associated with networked business collaborations
? Prototyping and application in real cases
P3
Software defined networks (SDN)
Fuzzy techniques, DEMATEL
? Reduction of the degree of uncertainty
? Associative risk management
P4
Railways
Fuzzy techniques
? Reduction in the degree of uncertainty
?Hierarchical factors for risk probability and impact assessment
Framework
F1
Electronic transactions
STOPE, DMAIC
? Risks associated with networked business collaborations
F2
IT projects
KM
? Application of knowledge management techniques to risk analysis
F3
Global
?
? Structuring of information prior to risk analysis
? Knowledge reuse
F4
Global
?
? Dynamic risk assessment
F5
Software development
Fuzzy techniques
? Reduction in the degree of uncertainty
F6
Global
?
? Knowledge reuse
?Importance of Dynamic Risk and Cloud Environments
F7
Critical Infrastructure
FMEA
? Reduction inf the degree of uncertainty
F8
Global
Logic trees
? Decision-making processes
?Knowledge reuse
?Targeting SMEs & Associative risk management
F9
Business processes
?
? Integration of BPM with risk management
?Risk metamodel
Model
MO1
Global
Theory of evidenceFuzzy measures
? Management of uncertainty in results
?Consistency evidence
? Case study
MO2
Global
VIKOR, DEMATEL, ANP
? Interdependence and feedback of risk criteria
? PDCA risk assessment process
MO3
Global
Bayesian Networks
? Weakness propagation uncertainty analysis
MO4
Global
?
? Decision-making processes
? Improved processes with which to analyse risk information
? Case study
MO5
Data
?
? Use of risk patterns
?Associative risk management
?Case study
MO6
Global
Logistic regression models
? Predictive techniques
?Dynamic risk assessment
?Focus on SMEs
MO7
Data
Heuristic techniques
? Dynamic risk assessment
?Importance of the environment and third parties in risk
MO8
Supply chains
Stochastic & Deterministic techniques
? Mixed application of stochastic and deterministic techniques
?Reduction in the degree of uncertainty
?Decision-making processes
MO9
ICS
Mind Maps
? Dynamic risk assessment
?Associative risk management
?Case study
Methodology
ME1
Global
Fuzzy techniques
? Reduction in the degree of uncertainty
? Importance of the environment and third parties in risk
ME2
Global
FMEA
? Reduction of uncertainty
ME3
Critical Infrastructure
Bow Tie Models, Bayesian Networks
? Concurrent analysis of risks and vulnerabilities
?Case study
ME4
IoT
?
? Exploitability value of vulnerabilities
? Degrees of vulnerability
ME5
Financial
?
? Adaptation to dynamic scenarios
?Focus on SMEs
?Prototyping and application in real cases
ME6
I4.0 - HMI
VIKOR, DEMATEL, ANPFuzzy techniques
? Reduction in the degree of uncertainty
?Importance of the environment in risk
Others
O1
Global
?
? Progressive improvement of risk levels
O2
Data Centre
?
? Risk analysis in virtualisation
?Risks in data centres outside but part of the IS
Tab.2
Type
Initiative
AC
HA
RKL
DY
CC
AE
DM
LLS
SLC
TS
GS
PC
Process
P1
No
Part.
No
No
No
Part.
No
No
No
No
No
No
P2
No
Part.
No
No
No
Part.
No
No
No
Yes
No
Yes
P3
No
Part.
No
No
No
Part.
No
Part.
No
No
No
Part.
P4
No
Part.
No
No
No
Part.
No
Part.
No
No
No
No
Framework
F1
No
Part.
No
No
No
No
No
No
No
No
No
No
F2
No
No
Yes
No
No
No
No
No
No
No
No
No
F3
No
Part.
Yes
No
No
No
No
No
No
No
Yes
No
F4
No
No
Part.
No
No
No
No
Part.
No
No
Yes
No
F5
No
No
No
No
No
Part.
No
Part.
No
No
No
No
F6
No
Part.
Yes
Part.
No
No
No
No
No
No
Yes
No
F7
No
No
No
No
No
Part.
No
Part.
No
No
Yes
No
F8
No
Part.
Yes
No
No
Part.
No
No
Yes
Yes
Yes
No
F9
Part.
No
No
No
No
No
No
No
No
Part.
Yes
Part.
Model
MO1
No
Part.
No
Part.
No
Part.
No
Part.
No
No
Yes
Yes
MO2
No
No
Part.
No
No
Part.
No
No
No
No
Yes
Yes
MO3
No
Part.
No
No
No
Part.
No
Part.
No
No
Yes
No
MO4
No
Part.
Part.
No
No
No
No
No
No
No
Yes
Yes
MO5
Part.
Part.
No
No
No
Part.
No
No
No
No
No
Part.
MO6
No
No
No
Part.
No
No
No
Part.
Part.
No
Yes
Yes
MO7
No
Part.
No
Part.
No
No
No
No
No
No
No
No
MO8
No
No
No
Part.
No
Part.
No
No
No
No
No
No
MO9
Part.
Yes
No
Yes
No
Part.
No
No
No
No
No
Yes
Methodology
ME1
No
Part.
No
Part.
No
Part.
No
Part.
No
No
Yes
No
ME2
No
Part.
No
No
No
Part.
No
Part.
No
No
Yes
No
ME3
No
No
No
No
No
No
No
No
No
No
No
Yes
ME4
No
No
No
No
No
Part.
No
No
No
No
No
Yes
ME5
No
No.
No
Yes
No
Part.
Yes
No
Yes
Yes
No
Yes
ME6
No
Parc.
No
No
No
No
No
Yes
No
No
No
No
Others
O1
No
No
Yes
No
No
Part.
No
No
No
No
Yes
No
O2
No
Part.
No
Part.
No
No
No
No
No
No
No
Part.
Tab.3
Fig.1
Fig.2
Fig.3
Fig.4
Fig.5
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