Author(s): Figueiredo, J. ; Serrão, C. ; de Almeida, A.
Date: 2023
Persistent ID: http://hdl.handle.net/10071/28099
Origin: Repositório ISCTE
Subject(s): Network intrusion detection system (NIDS); Intrusion detection; Anomaly detection; Deep learning (DL); Long short-term memory (LSTM)
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.
