@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}
}