@Article{jnm.2022.034778, AUTHOR = {Yongzhong Li, Cong Li, Yuheng Li, Shipeng Zhang}, TITLE = {A New Intrusion Detection Algorithm AE-3WD for Industrial Control Network}, JOURNAL = {Journal of New Media}, VOLUME = {4}, YEAR = {2022}, NUMBER = {4}, PAGES = {205--217}, URL = {http://www.techscience.com/JNM/v4n4/50711}, ISSN = {2579-0129}, ABSTRACT = {In this paper, we propose a intrusion detection algorithm based on auto-encoder and three-way decisions (AE-3WD) for industrial control networks, aiming at the security problem of industrial control network. The ideology of deep learning is similar to the idea of intrusion detection. Deep learning is a kind of intelligent algorithm and has the ability of automatically learning. It uses self-learning to enhance the experience and dynamic classification capabilities. We use deep learning to improve the intrusion detection rate and reduce the false alarm rate through learning, a denoising AutoEncoder and three-way decisions intrusion detection method AE-3WD is proposed to improve intrusion detection accuracy. In the processing, deep learning AutoEncoder is used to extract the features of high-dimensional data by combining the coefficient penalty and reconstruction loss function of the encode layer during the training mode. A multi-feature space can be constructed by multiple feature extractions from AutoEncoder, and then a decision for intrusion behavior or normal behavior is made by three-way decisions. NSL-KDD data sets are used to the experiments. The experiment results prove that our proposed method can extract meaningful features and effectively improve the performance of intrusion detection.}, DOI = {10.32604/jnm.2022.034778} }