Detection and Recognition of Abnormal Data Caused by Network Intrusion Using Deep Learning

Authors

  • Yan Jian
  • Xiaoyang Dong
  • Liang Jian

DOI:

https://doi.org/10.31449/inf.v45i3.3639

Abstract

Based on deep learning, this study combined sparse autoencoder (SAE) with extreme learning machine (ELM) to design an SAE-ELM method to reduce the dimension of data features and realize the classification of different types of data. Experiments were carried out on NSL-KDD and UNSW-NB2015 data sets. The results showed that, compared with the K-means algorithm and the SVM algorithm, the proposed method had higher performance. On the NSL-KDD data set, the average accuracy rate of the SAE-ELM method was 98.93%, the false alarm rate was 0.17%, and the missing report rate was 5.36%. On the UNSW-NB2015 data set, the accuracy rate of the SAE-ELM method was 98.88%, the false alarm rate was 0.12%, and the missing report rate was 4.31%. The results show that the SAE-ELM method is effective in the detection and recognition of abnormal data and can be popularized and applied.

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Published

2021-09-15

How to Cite

Jian, Y., Dong, X., & Jian, L. (2021). Detection and Recognition of Abnormal Data Caused by Network Intrusion Using Deep Learning. Informatica, 45(3). https://doi.org/10.31449/inf.v45i3.3639

Issue

Vol. 45 No. 3 (2021)

Section

Regular papers

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