Unsupervised textual anomaly detection, which discovers anomalies from unlabeled texts, is critical to improve the cybersecurity and interaction ability among the objects in the Social Internet of Things (SIoT). Recently, detecting anomalies by deep neural networks has become a popular trend. Specially, context vector data description (CVDD) method shows the promising performance. However, CVDD has two limitations: (1) it uses an one-class classification objective to constrain the sentence embeddings, which leads the learned embeddings to lose content information of text. (2) Scalar-based attention weights, which are used to extract sentence features, fail to focus on dimensional properties in a word. Learning the text contents and the dimensional properties is important for detection task in SIoT, which can help detector capture the difference between normal and anomaly texts. To overcome these limits, this paper proposes a textual anomaly detection network. First, an adversarial training strategy is designed to fight against the problem of missing content information. Second, a textual anomaly detection module with multiple dimensional transformation matrices is constructed to learn dimensional properties of words in diverse semantic subspaces. Experimental results on several textual datasets show that our proposed method outperforms CVDD and other strong baselines.

无监督文本异常检测从未标记的文本中发现异常，对于提高社交物联网 (SIoT) 中对象之间的网络安全和交互能力至关重要。最近，通过深度神经网络检测异常已成为一种流行趋势。特别是，上下文向量数据描述（CVDD）方法显示出良好的性能。然而，CVDD 有两个局限性：（1）它使用一类分类目标来约束句子嵌入，这导致学习到的嵌入丢失了文本的内容信息。(2) 用于提取句子特征的基于标量的注意力权重未能关注单词的维度属性。学习文本内容和维度属性对于 SIoT 中的检测任务很重要，这可以帮助检测器捕获正常和异常文本之间的差异。为了克服这些限制，本文提出了一种文本异常检测网络。首先，对抗性训练策略旨在解决缺少内容信息的问题。其次，构建了具有多维变换矩阵的文本异常检测模块，以学习不同语义子空间中单词的维属性。在几个文本数据集上的实验结果表明，我们提出的方法优于 CVDD 和其他强基线。构建了具有多维变换矩阵的文本异常检测模块，以学习不同语义子空间中单词的维属性。在几个文本数据集上的实验结果表明，我们提出的方法优于 CVDD 和其他强基线。构建了具有多维变换矩阵的文本异常检测模块，以学习不同语义子空间中单词的维属性。在几个文本数据集上的实验结果表明，我们提出的方法优于 CVDD 和其他强基线。