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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MDPI AG
2024-11-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/8/11/150 |
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| author | Vadim Tynchenko Alexander Lomazov Vadim Lomazov Dmitry Evsyukov Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Ivan Malashin |
| author_facet | Vadim Tynchenko Alexander Lomazov Vadim Lomazov Dmitry Evsyukov Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Ivan Malashin |
| author_sort | Vadim Tynchenko |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-01ad86a831c34133b550b6d9394ed16f |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-01ad86a831c34133b550b6d9394ed16f2024-11-26T17:51:11ZengMDPI AGBig Data and Cognitive Computing2504-22892024-11-0181115010.3390/bdcc8110150Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-MakingVadim Tynchenko0Alexander Lomazov1Vadim Lomazov2Dmitry Evsyukov3Vladimir Nelyub4Aleksei Borodulin5Andrei Gantimurov6Ivan Malashin7Artificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaDepartment of Data Analysis and Machine Learning, Financial University, 125167 Moscow, RussiaDepartment of Mathematics, Physics, Chemistry and Information Technologies, Belgorod State Agricultural University Named After V. Gorin, 308503 Belgorod, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaArtificial Intelligence Technology Scientific and Education Center, Bauman Moscow State Technical University, 105005 Moscow, RussiaIn 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.https://www.mdpi.com/2504-2289/8/11/150cybersecurityadaptive project managementscenario networkdecision supportlinguistic variablefuzzy production rule |
| spellingShingle | Vadim Tynchenko Alexander Lomazov Vadim Lomazov Dmitry Evsyukov Vladimir Nelyub Aleksei Borodulin Andrei Gantimurov Ivan Malashin Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making Big Data and Cognitive Computing cybersecurity adaptive project management scenario network decision support linguistic variable fuzzy production rule |
| title | Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making |
| title_full | Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making |
| title_fullStr | Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making |
| title_full_unstemmed | Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making |
| title_short | Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making |
| title_sort | adaptive management of multi scenario projects in cybersecurity models and algorithms for decision making |
| topic | cybersecurity adaptive project management scenario network decision support linguistic variable fuzzy production rule |
| url | https://www.mdpi.com/2504-2289/8/11/150 |
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