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In the information age, a secure and stable network environment is essentialand hence intrusion detection is critical for any networks. In this paper, wepropose a self-organizing map assisted deep autoencoding Gaussian mixture model(SOMDAGMM) supplemented with well-preserved input space topology for moreaccurate network intrusion detection. The deep autoencoding Gaussian mixturemodel comprises a compression network and an estimation network which is ableto perform unsupervised joint training. However, the code generated by theautoencoder is inept at preserving the topology of the input space, which isrooted in the bottleneck of the adopted deep structure. A self-organizing maphas been introduced to construct SOMDAGMM for addressing this issue. Thesuperiority of the proposed SOM-DAGMM is empirically demonstrated withextensive experiments conducted upon two datasets. Experimental results showthat SOM-DAGMM outperforms state-of-the-art DAGMM on all tests, and achieves upto 15.58% improvement in F1 score and with better stability.

Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection