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Figueiredo, J., Serrão, C. & de Almeida, A. (2023). Deep learning model transposition for network intrusion detection systems. Electronics, 12(2). https://doi.org/10.3390/electronics12020293

J. Figueiredo et al.,  "Deep learning model transposition for network intrusion detection systems", in Electronics, vol. 12, no. 2, 2023

@article{figueiredo2023_1789518739458,
	author = "Figueiredo, J. and Serrão, C. and de Almeida, A.",
	title = "Deep learning model transposition for network intrusion detection systems",
	journal = "Electronics",
	year = "2023",
	volume = "12",
	number = "2",
	doi = "10.3390/electronics12020293",
	url = "https://www.mdpi.com/2079-9292/12/2/293"
}

TY  - JOUR
TI  - Deep learning model transposition for network intrusion detection systems
T2  - Electronics
VL  - 12
IS  - 2
AU  - Figueiredo, J.
AU  - Serrão, C.
AU  - de Almeida, A.
PY  - 2023
SN  - 2079-9292
DO  - 10.3390/electronics12020293
UR  - https://www.mdpi.com/2079-9292/12/2/293
AB  - 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.
ER  -