论文标题
自组织地图辅助深度自动编码高斯混合模型用于入侵检测
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection
论文作者
论文摘要
在信息时代，安全稳定的网络环境至关重要，因此入侵检测对于任何网络至关重要。在本文中，我们提出了一个自组织的图辅助深度自动编码高斯混合模型（SOMDAGMM），并补充了保存完好的输入空间拓扑，以更准确地网络入侵检测。深度自动编码高斯混合物模型包括一个能够执行无监督关节训练的压缩网络和估计网络。但是，自动编码器生成的代码无能为力地保留输入空间的拓扑，该拓扑源于所采用的深层结构的瓶颈。已经引入了一个自组织地图来构建用于解决此问题的Somdagmm。通过在两个数据集上进行的广泛实验，在经验上证明了所提出的SOM-DAGMM的优势。实验结果表明，SOM-DAGMM在所有测试中的表现都优于最先进的dagmm，并且在F1分数上提高了15.58％，并且具有更好的稳定性。
In the information age, a secure and stable network environment is essential and hence intrusion detection is critical for any networks. In this paper, we propose a self-organizing map assisted deep autoencoding Gaussian mixture model (SOMDAGMM) supplemented with well-preserved input space topology for more accurate network intrusion detection. The deep autoencoding Gaussian mixture model comprises a compression network and an estimation network which is able to perform unsupervised joint training. However, the code generated by the autoencoder is inept at preserving the topology of the input space, which is rooted in the bottleneck of the adopted deep structure. A self-organizing map has been introduced to construct SOMDAGMM for addressing this issue. The superiority of the proposed SOM-DAGMM is empirically demonstrated with extensive experiments conducted upon two datasets. Experimental results show that SOM-DAGMM outperforms state-of-the-art DAGMM on all tests, and achieves up to 15.58% improvement in F1 score and with better stability.
