High-dimensional and unbalanced data anomaly detection is common. Effective anomaly detection is essential for problem or disaster early warning and maintaining system reliability. A significant research issue related to the data analysis of the sensor is the detection of anomalies. The anomaly detection is essentially an unbalanced sequence binary classification. The data of this type contains characteristics of large scale, high complex computation, unbalanced data distribution, and sequence relationship among data. This paper uses long short-term memory networks (LSTMs) combined with historical sequence data; also, it integrates the synthetic minority oversampling technique (SMOTE) algorithm and K-nearest neighbors (kNN), and it designs and constructs an anomaly detection network model based on kNN-SMOTE-LSTM in accordance with the data characteristic of being unbalanced. This model can continuously filter out and securely generate samples to improve the performance of the model through kNN discriminant classifier and avoid the blindness and limitations of the SMOTE algorithm in generating new samples. The experiments demonstrated that the structured kNN-SMOTE-LSTM model can significantly improve the performance of the unbalanced sequence binary classification.

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基于KNN-SMOTE-LSTM的高维非平衡数据异常检测模型的效果改进

高维和不平衡数据异常检测很常见。有效的异常检测对于问题或灾难早期预警以及维持系统可靠性至关重要。与传感器数据分析有关的重要研究问题是异常的检测。异常检测本质上是不平衡序列二进制分类。这种类型的数据具有大规模，高复杂度计算，不平衡的数据分布以及数据之间的顺序关系的特征。本文使用长短期记忆网络（LSTM）结合历史序列数据。此外，它还集成了综合少数群体过采样技术（SMOTE）算法和K近邻（kNN），根据不平衡的数据特征，设计并构建了基于kNN-SMOTE-LSTM的异常检测网络模型。该模型可以连续过滤并安全地生成样本，从而通过kNN判别器提高模型的性能，并避免了SMOTE算法在生成新样本时的盲目性和局限性。实验表明，结构化的kNN-SMOTE-LSTM模型可以显着提高不平衡序列二进制分类的性能。