<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.</p>
</abstract>

Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature extraction is also automated in these techniques. In this paper, an ensemble of different Deep Neural Network (DNN) models like MultiLayer Perceptron (MLP), BackPropagation Network (BPN) and Long Short Term Memory (LSTM) are stacked to build a robust anomaly detection model. The performance of the ensemble model is analysed on different datasets, namely UNSW-NB15 and a campus generated dataset named VIT_SPARC20. Other types of traffic, namely unencrypted normal traffic, normal encrypted traffic, encrypted and unencrypted malicious traffic, are captured in the VIT_SPARC20 dataset. Encrypted normal and malicious traffic of VIT_SPARC20 is categorised by the deep learning models without decrypting its contents, thus preserving the confidentiality and integrity of the data transmitted. XGBoost integrates the results of each deep learning model to achieve higher accuracy. From experimental analysis, it is inferred that UNSW_ NB results in a maximal accuracy of 99.5%. The performance of VIT_SPARC20 in terms of accuracy, precision and recall are 99.4%. 98% and 97%, respectively.

Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models"}

Intrusion Detection Systems (IDSs) utilise deep learning techniques to identify intrusions with maximum accuracy and reduce false alarm rates. The feature...

Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Data publikacji: 07 gru 2021

Zakres stron: 175 - 188

Otrzymano: 29 lip 2021

Przyjęty: 03 wrz 2021

DOI: https://doi.org/10.2478/cait-2021-0037

Słowa kluczowe
Accuracy, Backpropagation network, Intrusion detection, Multilayer perceptron, Long short term memory

© 2021 Sumaiya Thaseen Ikram et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Anomaly Detection Using XGBoost Ensemble of Deep Neural Network Models

Data publikacji: 07 gru 2021

Zakres stron: 175 - 188

Otrzymano: 29 lip 2021

Przyjęty: 03 wrz 2021

DOI: https://doi.org/10.2478/cait-2021-0037

Słowa kluczoweAccuracy, Backpropagation network, Intrusion detection, Multilayer perceptron, Long short term memory

© 2021 Sumaiya Thaseen Ikram et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Accuracy, Backpropagation network, Intrusion detection, Multilayer perceptron, Long short term memory