Please use this identifier to cite or link to this item:
https://hdl.handle.net/10316/114807
DC Field | Value | Language |
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dc.contributor.author | Figueiredo, João | - |
dc.contributor.author | Serrão, Carlos | - |
dc.contributor.author | Almeida, Ana Maria de | - |
dc.date.accessioned | 2024-04-12T10:22:56Z | - |
dc.date.available | 2024-04-12T10:22:56Z | - |
dc.date.issued | 2023 | - |
dc.identifier.issn | 2079-9292 | pt |
dc.identifier.uri | https://hdl.handle.net/10316/114807 | - |
dc.description.abstract | 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. | pt |
dc.language.iso | eng | pt |
dc.publisher | MDPI | pt |
dc.relation | UIDB/04466/2020 | pt |
dc.relation | UIDP/04466/2020 | pt |
dc.rights | openAccess | pt |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt |
dc.subject | network intrusion detection system (NIDS) | pt |
dc.subject | intrusion detection | pt |
dc.subject | anomaly detection | pt |
dc.subject | deep learning (DL) | pt |
dc.subject | long short-term memory (LSTM) | pt |
dc.title | Deep Learning Model Transposition for Network Intrusion Detection Systems | pt |
dc.type | article | - |
degois.publication.firstPage | 293 | pt |
degois.publication.issue | 2 | pt |
degois.publication.title | Electronics (Switzerland) | pt |
dc.peerreviewed | yes | pt |
dc.identifier.doi | 10.3390/electronics12020293 | pt |
degois.publication.volume | 12 | pt |
dc.date.embargo | 2023-01-01 | * |
uc.date.periodoEmbargo | 0 | pt |
item.openairetype | article | - |
item.fulltext | Com Texto completo | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
crisitem.project.grantno | Information Sciences, Technologies and Architecture Research Center | - |
crisitem.author.orcid | 0000-0001-9519-4634 | - |
Appears in Collections: | I&D CISUC - Artigos em Revistas Internacionais |
Files in This Item:
File | Description | Size | Format | |
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Deep-Learning-Model-Transposition-for-Network-Intrusion-Detection-SystemsElectronics-Switzerland.pdf | 418.3 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License