期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2020
卷号：47
期号：2
语种：English
出版社：IAENG - International Association of Engineers
摘要：Network intrusion detection has become a key technology to identify various network attacks. The traditional shallow methods based intrusion detection faces with the problem of ‘curse of dimensionality’ when computation happens in high-dimensional feature space. It fails to extract representative and abstract features from the high dimensional input, which reduces the detection accuracy. Therefore, an intrusion detection model based on deep learning framework with multi-layer extreme learning machine (ELM) is proposed. The proposed method is consisted of multiple extreme learning machine based auto-encoder (ELM-AE) in the front hidden layers and one ELM based classifier in the last hidden layer. The multiple ELM-AEs in the front hidden layers are utilized as unsupervised learning to extract deep features from the original input. Then the extracted features are substituted into the ELM in the last hidden layer as supervised learning to identify different types of attacks. The KDD99 dataset is utilized as the training and testing samples in the experiment. The results indicate that the detection accuracy of the proposed method is higher than some shallow methods (support vector machine and ELM), while the time consuming of the proposed method is much lower than the existing deep learning method (stacked auto-encoder).
