Hybrid AE-MLP: Hybrid Deep Learning Model Based on Autoencoder and Multilayer Perceptron Model for Intrusion Detection System
International journal of intelligent engineering and systems · 2023
Network violations are currently society's major challenge.For networks to be protected against hostile threats, an intrusion detection system (IDS) is important.To create effective IDS, deep learning (DL) is used in various fields, including information security.In this paper, a hybrid deep learning approach is proposed to effectively identify network intrusions using Autoencoder (AE) and Multi-layer perceptron (MLP).We use Autoencoder which can reduce the number of the original attributes based on the number of attributes, we first enter the original data on the Autoencoder and produce new compressed data, then enter it on the MLP classifier.The NSL-KDD dataset is thoroughly evaluated to determine the efficacy of the hybrid AE-MLP model the best outcomes are reached, with an accuracy rate of 87.6% and 81.06% (binary classification and multi-classification).In addition, the proposed hybrid method was compared with various recently proposed DL-based attack detection mechanisms.In terms of performance on the available dataset, it is observed that the proposed model outperformed.