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https://hdl.handle.net/10316/114807
Title: | Deep Learning Model Transposition for Network Intrusion Detection Systems | Authors: | Figueiredo, João Serrão, Carlos Almeida, Ana Maria de |
Keywords: | network intrusion detection system (NIDS); intrusion detection; anomaly detection; deep learning (DL); long short-term memory (LSTM) | Issue Date: | 2023 | Publisher: | MDPI | Project: | UIDB/04466/2020 UIDP/04466/2020 |
Serial title, monograph or event: | Electronics (Switzerland) | Volume: | 12 | Issue: | 2 | 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. | URI: | https://hdl.handle.net/10316/114807 | ISSN: | 2079-9292 | DOI: | 10.3390/electronics12020293 | Rights: | openAccess |
Appears in Collections: | I&D CISUC - Artigos em Revistas Internacionais |
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