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Applying a Neural Network Ensemble to Intrusion Detection
| Content Provider | Scilit |
|---|---|
| Author | Ludwig, Simone A. |
| Copyright Year | 2019 |
| Description | An intrusion detection system (IDS) is an important feature to employ in order to protect a system against network attacks. An IDS monitors the activity within a network of connected computers as to analyze the activity of intrusive patterns. In the event of an ‘attack’, the system has to respond appropriately. Different machine learning techniques have been applied in the past. These techniques fall either into the clustering or the classification category. In this paper, the classification method is used whereby a neural network ensemble method is employed to classify the different types of attacks. The neural network ensemble method consists of an autoencoder, a deep belief neural network, a deep neural network, and an extreme learning machine. The data used for the investigation is the NSL-KDD data set. In particular, the detection rate and false alarm rate among other measures (confusion matrix, classification accuracy, and AUC) of the implemented neural network ensemble are evaluated. |
| Related Links | https://content.sciendo.com/downloadpdf/journals/jaiscr/9/3/article-p177.pdf |
| e-ISSN | 20832567 |
| DOI | 10.2478/jaiscr-2019-0002 |
| Journal | Journal of Artificial Intelligence and Soft Computing Research |
| Issue Number | 3 |
| Volume Number | 9 |
| Language | English |
| Publisher | Walter de Gruyter GmbH |
| Publisher Date | 2019-05-09 |
| Access Restriction | Open |
| Subject Keyword | Journal: Journal of Artificial Intelligence and Soft Computing Research Journal of Artificial Intelligence and Soft Computing Research Hardware and Architecturee Intrusion Detection Neural Network Ensemble |
| Content Type | Text |
| Resource Type | Article |
