MuDeLA: multi-level deep learning approach for intrusion detection systems

Abstract

In recent years, deep learning techniques have achieved significant results in several fields, like computer vision, speech recognition, bioinformatics, medical image analysis, and natural language processing. On the other hand, deep learning for intrusion detection has been widely used, particularly the implementation of convolutional neural networks (CNN), multilayer perceptron (MLP), and autoencoders (AE) to classify normal and abnormal. In this article, we propose a multi-level deep learning approach (MuDeLA) for intrusion detection systems (IDS). The MuDeLA is based on CNN and MLP to enhance the performance of detecting attacks in the IDS. The MuDeLA is evaluated by using various well-known benchmark datasets like KDDCup'99, NSL-KDD, and UNSW-NB15 in order to expand the comparison with different related work results. The outcomes show that the proposed MuDeLA achieves high efficiency for multiclass classification compared with the other methods, where the accuracy reaches 95.55 for KDDCup'99, 88.12 for NSL-KDD, and 90.52 for UNSW-NB15.