Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making

In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article...

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Vydáno v:Big data and cognitive computing Ročník 8; číslo 11; s. 150
Hlavní autoři: Tynchenko, Vadim, Lomazov, Alexander, Lomazov, Vadim, Evsyukov, Dmitry, Nelyub, Vladimir, Borodulin, Aleksei, Gantimurov, Andrei, Malashin, Ivan
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.11.2024
Témata:
Access control
Adaptability
Adaptive algorithms
adaptive project management
Algorithms
Artificial intelligence
Computational linguistics
Continuity (mathematics)
Cybersecurity
Cyberterrorism
Data mining
Data security
Decision analysis
Decision making
decision support
Document management
Errors
Experiments
Expert systems
Forecasts and trends
Fuzzy logic
fuzzy production rule
Genetic algorithms
Genetics
High risk
Inference
Intelligence
Internet
Language processing
linguistic variable
Linguistics
Malware
Natural language interfaces
Natural language processing
Networks
Neural networks
Performance management
Project management
Projections
Prototypes
Rules
Safety and security measures
scenario network
Scenario planning
Security management
Taxonomy
Theoretical linguistics
Threats
Trajectory planning
Uncertainty
Variables
Russia
ISSN:2504-2289, 2504-2289
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Shrnutí:In recent years, cybersecurity management has increasingly required advanced methodologies capable of handling complex, evolving threat landscapes. Scenario network-based approaches have emerged as effective strategies for managing uncertainty and adaptability in cybersecurity projects. This article introduces a scenario network-based approach for managing cybersecurity projects, utilizing fuzzy linguistic models and a Takagi–Sugeno–Kanga fuzzy neural network. Drawing upon L. Zadeh’s theory of linguistic variables, the methodology integrates expert analysis, linguistic variables, and a continuous genetic algorithm to predict membership function parameters. Fuzzy production rules are employed for decision-making, while the Mamdani fuzzy inference algorithm enhances interpretability. This approach enables multi-scenario planning and adaptability across multi-stage cybersecurity projects. Preliminary results from a research prototype of an intelligent expert system—designed to analyze project stages and adaptively construct project trajectories—suggest the proposed approach is effective. In computational experiments, the use of fuzzy procedures resulted in an over 25% reduction in errors compared to traditional methods, particularly in adjusting project scenarios from pessimistic to baseline projections. While promising, this approach requires further testing across diverse cybersecurity contexts. Future studies will aim to refine scenario adaptation and optimize system response in high-risk project environments.
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ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc8110150