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An intrusion detection approach based on improved deep belief network

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Abstract

In today’s interconnected society, cyberattacks have become more frequent and sophisticated, and existing intrusion detection systems may not be adequate in the complex cyberthreat landscape. For instance, existing intrusion detection systems may have overfitting, low classification accuracy, and high false positive rate (FPR) when faced with significantly large volume and variety of network data. An intrusion detection approach based on improved deep belief network (DBN) is proposed in this paper to mitigate the above problems, where the dataset is processed by probabilistic mass function (PMF) encoding and Min-Max normalization method to simplify the data preprocessing. Furthermore, a combined sparsity penalty term based on Kullback-Leibler (KL) divergence and non-mean Gaussian distribution is introduced in the likelihood function of the unsupervised training phase of DBN, and sparse constraints retrieve the sparse distribution of the dataset, thus avoiding the problem of feature homogeneity and overfitting. Finally, simulation experiments are performed on the NSL-KDD and UNSW-NB15 public datasets. The proposed method achieves 96.17% and 86.49% accuracy, respectively. Experimental results show that compared with the state-of-the-art methods, the proposed method achieves significant improvement in classification accuracy and FPR.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61672338 and No. 61873160).

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Authors and Affiliations

  1. College of Information Engineering, Shanghai Maritime University, Shanghai, China

    Qiuting Tian, Dezhi Han, Xingao Liu & Letian Duan

  2. Department of Computer Science and Information Engineering (CSIE), Providence University, Taichung City, Taiwan

    Kuan-Ching Li

  3. Department of Computer Science, University of Salerno, Fisciano, SA, Italy

    Arcangelo Castiglione

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  1. Qiuting Tian
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  2. Dezhi Han
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  3. Kuan-Ching Li
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  4. Xingao Liu
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  6. Arcangelo Castiglione
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Correspondence to Kuan-Ching Li.

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Tian, Q., Han, D., Li, KC. et al. An intrusion detection approach based on improved deep belief network. Appl Intell 50, 3162–3178 (2020). https://doi.org/10.1007/s10489-020-01694-4

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