Entropy-extreme model for predicting the development of cyber epidemics at early stages
The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal commu...
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Elsevier
2024-12-01
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| Series: | Computational and Structural Biotechnology Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037024002770 |
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| author | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Wojciech Kempa |
| author_facet | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Wojciech Kempa |
| author_sort | Viacheslav Kovtun |
| collection | DOAJ |
| description | The approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters’ values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data. |
| format | Article |
| id | doaj-art-bca9cef91a4f4ab6b4a67611511f983f |
| institution | Kabale University |
| issn | 2001-0370 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj-art-bca9cef91a4f4ab6b4a67611511f983f2024-12-19T10:53:27ZengElsevierComputational and Structural Biotechnology Journal2001-03702024-12-0124593602Entropy-extreme model for predicting the development of cyber epidemics at early stagesViacheslav Kovtun0Krzysztof Grochla1Mohammed Al-Maitah2Saad Aldosary3Wojciech Kempa4Computer Control Systems Department, Faculty of Intelligent Information Technologies and Automation, Vinnytsia National Technical University, Khmelnitske Shose str., 95, Vinnytsia 21000, Ukraine; Corresponding author.Internet of Things Group, Institute of Theoretical and Applied Informatics Polish Academy of Sciences, Bałtycka 5, 44-100 Gliwice, PolandComputer Science Department, Community College, King Saud University, 11451 Riyadh, Saudi ArabiaComputer Science Department, Community College, King Saud University, 11451 Riyadh, Saudi ArabiaDepartment of Mathematics Applications and Methods for Artificial Intelligence, Faculty of Applied Mathematics, Silesian University of Technology, Ul. Akademicka 2 A, 44-100 Gliwice, PolandThe approaches used in biomedicine to analyze epidemics take into account features such as exponential growth in the early stages, slowdown in dynamics upon saturation, time delays in spread, segmented spread, evolutionary adaptations of the pathogen, and preventive measures based on universal communication protocols. All these characteristics are also present in modern cyber epidemics. Therefore, adapting effective biomedical approaches to epidemic analysis for the investigation of the development of cyber epidemics is a promising scientific research task. The article is dedicated to researching the problem of predicting the development of cyber epidemics at early stages. In such conditions, the available data is scarce, incomplete, and distorted. This situation makes it impossible to use artificial intelligence models for prediction. Therefore, the authors propose an entropy-extreme model, defined within the machine learning paradigm, to address this problem. The model is based on estimating the probability distributions of its controllable parameters from input data, taking into account the variability characteristic of the last ones. The entropy-extreme instance, identified from a set of such distributions, indicates the most uncertain (most negative) trajectory of the investigated process. Numerical methods are used to analyze the generated set of investigated process development trajectories based on the assessments of probability distributions of the controllable parameters and the variability characteristic. The result of the analysis includes characteristic predictive trajectories such as the average and median trajectories from the set, as well as the trajectory corresponding to the standard deviation area of the parameters’ values. Experiments with real data on the infection of Windows-operated devices by various categories of malware showed that the proposed model outperforms the classical competitor (least squares method) in predicting the development of cyber epidemics near the extremum of the time series representing the deployment of such a process over time. Moreover, the proposed model can be applied without any prior hypotheses regarding the probabilistic properties of the available data.http://www.sciencedirect.com/science/article/pii/S2001037024002770Cyber epidemicsMalwarePredictionSmall dataMachine learningEntropy-extreme model |
| spellingShingle | Viacheslav Kovtun Krzysztof Grochla Mohammed Al-Maitah Saad Aldosary Wojciech Kempa Entropy-extreme model for predicting the development of cyber epidemics at early stages Computational and Structural Biotechnology Journal Cyber epidemics Malware Prediction Small data Machine learning Entropy-extreme model |
| title | Entropy-extreme model for predicting the development of cyber epidemics at early stages |
| title_full | Entropy-extreme model for predicting the development of cyber epidemics at early stages |
| title_fullStr | Entropy-extreme model for predicting the development of cyber epidemics at early stages |
| title_full_unstemmed | Entropy-extreme model for predicting the development of cyber epidemics at early stages |
| title_short | Entropy-extreme model for predicting the development of cyber epidemics at early stages |
| title_sort | entropy extreme model for predicting the development of cyber epidemics at early stages |
| topic | Cyber epidemics Malware Prediction Small data Machine learning Entropy-extreme model |
| url | http://www.sciencedirect.com/science/article/pii/S2001037024002770 |
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