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

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