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A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System
2022
Mathematical Problems in Engineering
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. The 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
doi:10.1155/2022/3859155
fatcat:ku6u6x7vnramjbzu2v5asjysg4