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Ensemble Model Based on an Improved Convolutional Neural Network with a Domain-agnostic Data Augmentation Technique

Authors :

Faraz Fatahnaie¹ Armin Azhdehnia² Seyyed Amir Asghari³ Mohammadreza Binesh Marvasti⁴

1- دانشگاه خوارزمی 2- دانشگاه گیلان 3- دانشگاه خوارزمی 4- دانشگاه خوارزمی

Keywords :

Intrusion Detection System،NSL-KDD،Deep Learning،Ensemble Learning،Random Under Sampling،Data Augmentation

Abstract :

With the increase of online activities and the growing number of online services, various cyber threats pose a significant challenge to Network Intrusion Detection systems (NIDS). To face these threats, available imbalance sources made researchers develop resampling techniques to have a balance training process. In this paper, a domain-agnostic data augmentation approach followed by random under sampling is used to achieve credible generalized and robust IDS. Moreover, the framework benefits from the potentiality of deep learning models to extract more meaningful features. The final model of the paper was obtained after the ensemble of three improved convolutional neural networks. Each model is trained on a specific subset of NSL-KDD dataset which is generated by the resampling method. The simulation results illustrate that the model achieves an accuracy of 83.3% which is 6.5% higher, when the original dataset is used.

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