Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/28099
Autoria: | Figueiredo, J. Serrão, C. de Almeida, A. |
Data: | 2023 |
Título próprio: | Deep learning model transposition for network intrusion detection systems |
Título da revista: | Electronics |
Volume: | 12 |
Número: | 2 |
Referência bibliográfica: | Figueiredo, J., Serrão, C., & de Almeida, A. (2023). Deep learning model transposition for network intrusion detection systems. Electronics, 12(2), 293. http://dx.doi.org/10.3390/electronics12020293 |
ISSN: | 2079-9292 |
DOI (Digital Object Identifier): | 10.3390/electronics12020293 |
Palavras-chave: | Network intrusion detection system (NIDS) Intrusion detection Anomaly detection Deep learning (DL) Long short-term memory (LSTM) |
Resumo: | Companies seek to promote a swift digitalization of their business processes and new disruptive features to gain an advantage over their competitors. This often results in a wider attack surface that may be exposed to exploitation from adversaries. As budgets are thin, one of the most popular security solutions CISOs choose to invest in is Network-based Intrusion Detection Systems (NIDS). As anomaly-based NIDS work over a baseline of normal and expected activity, one of the key areas of development is the training of deep learning classification models robust enough so that, given a different network context, the system is still capable of high rate accuracy for intrusion detection. In this study, we propose an anomaly-based NIDS using a deep learning stacked-LSTM model with a novel pre-processing technique that gives it context-free features and outperforms most related works, obtaining over 99% accuracy over the CICIDS2017 dataset. This system can also be applied to different environments without losing its accuracy due to its basis on context-free features. Moreover, using synthetic network attacks, it has been shown that this NIDS approach can detect specific categories of attacks. |
Arbitragem científica: | yes |
Acesso: | Acesso Aberto |
Aparece nas coleções: | ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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article_92865.pdf | 418,3 kB | Adobe PDF | Ver/Abrir |
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