An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection

Brunner, Csaba, Kő, Andrea and Fodor, Szabina (2022) An Autoencoder-Enhanced Stacking Neural Network Model for Increasing the Performance of Intrusion Detection. Journal of Artificial Intelligence and Soft Computing Research, 12 (2). pp. 149-163. DOI

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Security threats, among other intrusions affecting the availability, confidentiality and integrity of IT resources and services, are spreading fast and can cause serious harm to organizations. Intrusion detection has a key role in capturing intrusions. In particular, the application of machine learning methods in this area can enrich the intrusion detection efficiency. Various methods, such as pattern recognition from event logs, can be applied in intrusion detection. The main goal of our research is to present a possible intrusion detection approach using recent machine learning techniques. In this paper, we suggest and evaluate the usage of stacked ensembles consisting of neural network (SNN) and autoencoder (AE) models augmented with a tree-structured Parzen estimator hyperparameter optimization approach for intrusion detection. The main contribution of our work is the application of advanced hyperparameter optimization and stacked ensembles together. We conducted several experiments to check the effectiveness of our approach. We used the NSL-KDD dataset, a common benchmark dataset in intrusion detection, to train our models. The comparative results demonstrate that our proposed models can compete with and, in some cases, outperform existing models.

Item Type:Article
Uncontrolled Keywords:intrusion detection, neural network, ensemble classifiers, hyperparameter optimization, sparse autoencoder, NSL-KDD, machine learning
Subjects:Knowledge economy, innovation
Automatizálás, gépesítés
Computer science
ID Code:7276
Deposited By: MTMT SWORD
Deposited On:18 Mar 2022 11:17
Last Modified:18 Mar 2022 11:17

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