Joohwa Lee, Keehyun Park

KIPS Transactions on Software and Data Engineering, Vol. 8, No. 12, pp. 483-490, Dec. 2019
https://doi.org/10.3745/KTSDE.2019.8.12.483,   PDF Download:
Keywords: NIDS, IoT, Unsupervised learning, Machine Learning, Autoencoder
Abstract

In the Network Intrusion Detection System (NIDS), the function of classification is very important, and detection performance depends on various features. Recently, a lot of research has been carried out on deep learning, but network intrusion detection system experience slowing down problems due to the large volume of traffic and a high dimensional features. Therefore, we do not use deep learning as a classification, but as a preprocessing process for feature extraction and propose a research method from which classifications can be made based on extracted features. A stacked AutoEncoder, which is a representative unsupervised learning of deep learning, is used to extract features and classifications using the Random Forest classification algorithm. Using the data collected in the IOT environment, the performance was more than 99% when normal and attack traffic are classified into multiclass, and the performance and detection rate were superior even when compared with other models such as AE-RF and Single-RF.

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