Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913899368939520 |
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| author | Miranda, Luan Gonçalves da Cruz, Pedro Ivo Loiola, Murilo Bellezoni |
| author_facet | Miranda, Luan Gonçalves da Cruz, Pedro Ivo Loiola, Murilo Bellezoni |
| contents | Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders (AE) show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction error, the definition of this threshold directly impacts the performance of the detection process. Thus, this work proposes the automatic definition of this threshold using some machine learning algorithms. For this, three algorithms were evaluated: the K-Nearst Neighbors, the K-Means and the Support Vector Machine. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14937 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders Miranda, Luan Gonçalves da Cruz, Pedro Ivo Loiola, Murilo Bellezoni Machine Learning Artificial Intelligence Cryptography and Security Networking and Internet Architecture Performance Currently, digital security mechanisms like Anomaly Detection Systems using Autoencoders (AE) show great potential for bypassing problems intrinsic to the data, such as data imbalance. Because AE use a non-trivial and nonstandardized separation threshold to classify the extracted reconstruction error, the definition of this threshold directly impacts the performance of the detection process. Thus, this work proposes the automatic definition of this threshold using some machine learning algorithms. For this, three algorithms were evaluated: the K-Nearst Neighbors, the K-Means and the Support Vector Machine. |
| title | Determinação Automática de Limiar de Detecção de Ataques em Redes de Computadores Utilizando Autoencoders |
| topic | Machine Learning Artificial Intelligence Cryptography and Security Networking and Internet Architecture Performance |
| url | https://arxiv.org/abs/2506.14937 |