Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches
Cybersecurity has become an increasing priority in large organizations due to the rapid evolution of digital threats. With increasingly sophisticated attacks, vulnerability management emerges as one of the main strategies to protect systems and data. However, the complexity and dynamics of threats m...
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| Format: | Article |
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10945367/ |
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| author | Juscimara G. Avelino Bruna A. O. Brito Thiago T. de Oliveira Jean M. M. de Lima Andre M. Gurgel Ramon S. Malaquias Itamir de M. B. Filho |
| author_facet | Juscimara G. Avelino Bruna A. O. Brito Thiago T. de Oliveira Jean M. M. de Lima Andre M. Gurgel Ramon S. Malaquias Itamir de M. B. Filho |
| author_sort | Juscimara G. Avelino |
| collection | DOAJ |
| description | Cybersecurity has become an increasing priority in large organizations due to the rapid evolution of digital threats. With increasingly sophisticated attacks, vulnerability management emerges as one of the main strategies to protect systems and data. However, the complexity and dynamics of threats make management processes challenging, requiring efficient and adaptable approaches. Artificial Intelligence (AI) and Machine Learning (ML) are promising tools to anticipate and mitigate these threats, offering predictive capabilities based on historical data. This article proposes the use of time series in a hierarchical manner for the forecasting of vulnerabilities in systems. The proposed methodology aims to deal with the complexity of the data, allowing a hierarchical structure to understand and capture the interdependencies and patterns among different levels more completely. The evaluation is carried out with different ML models, such as LSTM, RNN, MLP, among others, comparing the performance of hierarchical and non-hierarchical approaches. The results indicate that, in a hierarchical structure, especially LSTM, shows superior performance in vulnerability forecasting. However, in some scenarios, when using reconcilers, models like N-BEATS also demonstrate good results. |
| format | Article |
| id | doaj-art-dd9ed0cbd4a74cb7a5acac7d48ffd27f |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-dd9ed0cbd4a74cb7a5acac7d48ffd27f2025-08-20T03:17:44ZengIEEEIEEE Access2169-35362025-01-0113586585867010.1109/ACCESS.2025.355586010945367Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical ApproachesJuscimara G. Avelino0https://orcid.org/0000-0002-6934-0746Bruna A. O. Brito1https://orcid.org/0009-0001-8116-495XThiago T. de Oliveira2https://orcid.org/0009-0006-7457-5941Jean M. M. de Lima3https://orcid.org/0000-0002-2324-9365Andre M. Gurgel4https://orcid.org/0000-0002-1925-8031Ramon S. Malaquias5https://orcid.org/0000-0002-8350-2836Itamir de M. B. Filho6https://orcid.org/0000-0003-1694-8237Digital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilDigital Metropolis Institute, Federal University of Rio Grande do Norte, Natal, Rio Grande do Norte, BrazilCybersecurity has become an increasing priority in large organizations due to the rapid evolution of digital threats. With increasingly sophisticated attacks, vulnerability management emerges as one of the main strategies to protect systems and data. However, the complexity and dynamics of threats make management processes challenging, requiring efficient and adaptable approaches. Artificial Intelligence (AI) and Machine Learning (ML) are promising tools to anticipate and mitigate these threats, offering predictive capabilities based on historical data. This article proposes the use of time series in a hierarchical manner for the forecasting of vulnerabilities in systems. The proposed methodology aims to deal with the complexity of the data, allowing a hierarchical structure to understand and capture the interdependencies and patterns among different levels more completely. The evaluation is carried out with different ML models, such as LSTM, RNN, MLP, among others, comparing the performance of hierarchical and non-hierarchical approaches. The results indicate that, in a hierarchical structure, especially LSTM, shows superior performance in vulnerability forecasting. However, in some scenarios, when using reconcilers, models like N-BEATS also demonstrate good results.https://ieeexplore.ieee.org/document/10945367/Cybersecuritymachine learninghierarchical time seriesvulnerability forecasting |
| spellingShingle | Juscimara G. Avelino Bruna A. O. Brito Thiago T. de Oliveira Jean M. M. de Lima Andre M. Gurgel Ramon S. Malaquias Itamir de M. B. Filho Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches IEEE Access Cybersecurity machine learning hierarchical time series vulnerability forecasting |
| title | Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches |
| title_full | Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches |
| title_fullStr | Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches |
| title_full_unstemmed | Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches |
| title_short | Vulnerability Time Series Forecasting: A Comparative Study of Hierarchical and Non-Hierarchical Approaches |
| title_sort | vulnerability time series forecasting a comparative study of hierarchical and non hierarchical approaches |
| topic | Cybersecurity machine learning hierarchical time series vulnerability forecasting |
| url | https://ieeexplore.ieee.org/document/10945367/ |
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