Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/9/1967 |
| _version_ | 1811304041131540480 |
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| author | Manuel Lopez-Martin Belen Carro Antonio Sanchez-Esguevillas Jaime Lloret |
| author_facet | Manuel Lopez-Martin Belen Carro Antonio Sanchez-Esguevillas Jaime Lloret |
| author_sort | Manuel Lopez-Martin |
| collection | DOAJ |
| description | The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. |
| first_indexed | 2024-04-13T07:58:49Z |
| format | Article |
| id | doaj.art-2f10f2590a5c4e7eac43d55efb06775d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-13T07:58:49Z |
| publishDate | 2017-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2f10f2590a5c4e7eac43d55efb06775d2022-12-22T02:55:19ZengMDPI AGSensors1424-82202017-08-01179196710.3390/s17091967s17091967Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoTManuel Lopez-Martin0Belen Carro1Antonio Sanchez-Esguevillas2Jaime Lloret3Dpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, SpainDpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, SpainDpto. TSyCeIT, ETSIT, Universidad de Valladolid, Paseo de Belén 15, 47011 Valladolid, SpainInstituto de Investigación para la Gestión Integrada de Zonas Costeras, Universitat Politècnica de València, Camino Vera s/n, 46022 Valencia, SpainThe purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.https://www.mdpi.com/1424-8220/17/9/1967intrusion detectionvariational methodsconditional variational autoencoderfeature recoveryneural networks |
| spellingShingle | Manuel Lopez-Martin Belen Carro Antonio Sanchez-Esguevillas Jaime Lloret Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT Sensors intrusion detection variational methods conditional variational autoencoder feature recovery neural networks |
| title | Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT |
| title_full | Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT |
| title_fullStr | Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT |
| title_full_unstemmed | Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT |
| title_short | Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT |
| title_sort | conditional variational autoencoder for prediction and feature recovery applied to intrusion detection in iot |
| topic | intrusion detection variational methods conditional variational autoencoder feature recovery neural networks |
| url | https://www.mdpi.com/1424-8220/17/9/1967 |
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