A metaheuristic autoencoder deep learning model for intrusion detector system
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Date
2022
Journal Title
Journal ISSN
Volume Title
Publisher
Mathematical Problems in Engineering
Abstract
A multichannel autoencoder deep learning approach is developed to address the present intrusion detection systems’ detection
accuracy and false alarm rate. First, two separate autoencoders are trained with average traffic and assault traffic. )e original
samples and the two additional feature vectors comprise a multichannel feature vector. Next, a one-dimensional convolution
neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic.
Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an
effective intrusion detection model. )e scope of this research is that the method described in this study may significantly
minimize false positives while also improving the detection accuracy of unknown attacks, which is the focus of this paper. )is
research was done in order to improve intrusion detection prediction performance. )e autoencoder can successfully reduce the
number of features while also allowing for easy integration with different neural networks; it can reduce the time it takes to train a
model while also improving its detection accuracy. An evolutionary algorithm is utilized to discover the ideal topology set of the
CNN model to maximize the hyperparameters and improve the network’s capacity to recognize interchannel dependencies. )is
paper is based on the multichannel autoencoder’s effectiveness; the fourth experiment is a comparative analysis, which proves the
benefits of the approach in this article by correlating it to the findings of various different intrusion detection methods. )is
technique outperforms previous intrusion detection algorithms in several datasets and has superior forecast accuracy.