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Front. Comput. Sci.    2024, Vol. 18 Issue (3) : 183808    https://doi.org/10.1007/s11704-023-1582-6
REVIEW ARTICLE
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
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Abstract

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.
Keywords information security management      security system      security risk assessment and management     
Corresponding Author(s): Antonio SANTOS-OLMO   
Just Accepted Date: 10 February 2023   Issue Date: 12 May 2023
 Cite this article:   
Antonio SANTOS-OLMO,Luis Enrique SáNCHEZ,David G. ROSADO, et al. Towards an integrated risk analysis security framework according to a systematic analysis of existing proposals[J]. Front. Comput. Sci., 2024, 18(3): 183808.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-023-1582-6
https://academic.hep.com.cn/fcs/EN/Y2024/V18/I3/183808
  
  
  
Initiative typeStudies #Initiatives
Process4P1, P2, P.3, P.4
Framework9F1, F2, F3, F4, F5, F6, F7, F8, F9
Model9MO1, MO2, MO3, MO4, MO5, MO6, MO7, MO8, MO9
Methodology6ME1, ME2, ME3, ME4, ME5, ME6
Others2O1, O2
Total30?
Tab.1  Results by initiative
TypeInitiat.ScopeTechniqueMain contributions
ProcessP1Limited ISO27001DEMATEL, ANP? Interrelationships between risks according to control areas
P2NetworksFuzzy techniques? Risks associated with networked business collaborations
? Prototyping and application in real cases
P3Software defined networks (SDN)Fuzzy techniques, DEMATEL? Reduction of the degree of uncertainty
? Associative risk management
P4RailwaysFuzzy techniques? Reduction in the degree of uncertainty
?Hierarchical factors for risk probability and impact assessment
FrameworkF1Electronic transactionsSTOPE, DMAIC? Risks associated with networked business collaborations
F2IT projectsKM? Application of knowledge management techniques to risk analysis
F3Global?? Structuring of information prior to risk analysis
? Knowledge reuse
F4Global?? Dynamic risk assessment
F5Software developmentFuzzy techniques? Reduction in the degree of uncertainty
F6Global?? Knowledge reuse
?Importance of Dynamic Risk and Cloud Environments
F7Critical InfrastructureFMEA? Reduction inf the degree of uncertainty
F8GlobalLogic trees? Decision-making processes
?Knowledge reuse
?Targeting SMEs & Associative risk management
F9Business processes?? Integration of BPM with risk management
?Risk metamodel
ModelMO1GlobalTheory of evidenceFuzzy measures? Management of uncertainty in results
?Consistency evidence
? Case study
MO2GlobalVIKOR, DEMATEL, ANP? Interdependence and feedback of risk criteria
? PDCA risk assessment process
MO3GlobalBayesian Networks? Weakness propagation uncertainty analysis
MO4Global?? Decision-making processes
? Improved processes with which to analyse risk information
? Case study
MO5Data?? Use of risk patterns
?Associative risk management
?Case study
MO6GlobalLogistic regression models? Predictive techniques
?Dynamic risk assessment
?Focus on SMEs
MO7DataHeuristic techniques? Dynamic risk assessment
?Importance of the environment and third parties in risk
MO8Supply chainsStochastic & Deterministic techniques? Mixed application of stochastic and deterministic techniques
?Reduction in the degree of uncertainty
?Decision-making processes
MO9ICSMind Maps? Dynamic risk assessment
?Associative risk management
?Case study
MethodologyME1GlobalFuzzy techniques? Reduction in the degree of uncertainty
? Importance of the environment and third parties in risk
ME2GlobalFMEA? Reduction of uncertainty
ME3Critical InfrastructureBow Tie Models, Bayesian Networks? Concurrent analysis of risks and vulnerabilities
?Case study
ME4IoT?? Exploitability value of vulnerabilities
? Degrees of vulnerability
ME5Financial?? Adaptation to dynamic scenarios
?Focus on SMEs
?Prototyping and application in real cases
ME6I4.0 - HMIVIKOR, DEMATEL, ANPFuzzy techniques? Reduction in the degree of uncertainty
?Importance of the environment in risk
OthersO1Global?? Progressive improvement of risk levels
O2Data Centre?? Risk analysis in virtualisation
?Risks in data centres outside but part of the IS
Tab.2  Main contributions of the selected proposals
TypeInitiativeACHARKLDYCCAEDMLLSSLCTSGSPC
ProcessP1NoPart.NoNoNoPart.NoNoNoNoNoNo
P2NoPart.NoNoNoPart.NoNoNoYesNoYes
P3NoPart.NoNoNoPart.NoPart.NoNoNoPart.
P4NoPart.NoNoNoPart.NoPart.NoNoNoNo
FrameworkF1NoPart.NoNoNoNoNoNoNoNoNoNo
F2NoNoYesNoNoNoNoNoNoNoNoNo
F3NoPart.YesNoNoNoNoNoNoNoYesNo
F4NoNoPart.NoNoNoNoPart.NoNoYesNo
F5NoNoNoNoNoPart.NoPart.NoNoNoNo
F6NoPart.YesPart.NoNoNoNoNoNoYesNo
F7NoNoNoNoNoPart.NoPart.NoNoYesNo
F8NoPart.YesNoNoPart.NoNoYesYesYesNo
F9Part.NoNoNoNoNoNoNoNoPart.YesPart.
ModelMO1NoPart.NoPart.NoPart.NoPart.NoNoYesYes
MO2NoNoPart.NoNoPart.NoNoNoNoYesYes
MO3NoPart.NoNoNoPart.NoPart.NoNoYesNo
MO4NoPart.Part.NoNoNoNoNoNoNoYesYes
MO5Part.Part.NoNoNoPart.NoNoNoNoNoPart.
MO6NoNoNoPart.NoNoNoPart.Part.NoYesYes
MO7NoPart.NoPart.NoNoNoNoNoNoNoNo
MO8NoNoNoPart.NoPart.NoNoNoNoNoNo
MO9Part.YesNoYesNoPart.NoNoNoNoNoYes
MethodologyME1NoPart.NoPart.NoPart.NoPart.NoNoYesNo
ME2NoPart.NoNoNoPart.NoPart.NoNoYesNo
ME3NoNoNoNoNoNoNoNoNoNoNoYes
ME4NoNoNoNoNoPart.NoNoNoNoNoYes
ME5NoNo.NoYesNoPart.YesNoYesYesNoYes
ME6NoParc.NoNoNoNoNoYesNoNoNoNo
OthersO1NoNoYesNoNoPart.NoNoNoNoYesNo
O2NoPart.NoPart.NoNoNoNoNoNoNoPart.
Tab.3  Comparison of the selected proposals
Fig.1  MARISMA methodology
Fig.2  CAT meta-pattern
Fig.3  Overview in SPEM of the processes in the methodology
Fig.4  Example of audits tree of eMARISMA tool
Fig.5  Diagram of eMARISMA architecture
  
  
  
  
  
  
  
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