Anomaly detection models based on context-aware sequential long short-term memory learning

For a large and complex system that provides services to users, an exception can cause cascading failures if it is not detected and handled in time. System monitoring and anomaly detection can be used to identify system malfunctioning. However, as the size and the complexity of the online service system increases, anomaly detection becomes a challenging problem. This is because the size, complexity and correlation among the data bring great difficulties to anomaly detection process. To address the above challenges, we propose three context-aware sequential Long Short-Term Memory (LSTM) learning models for multi-dimensional anomaly detection, namely, LastLSTM model, AvgLSTM model and CirclLSTM model. In particular, the CirclLSTM model is a period-related LSTM model that can integrate cyclical system historical information into anomaly learning. We evaluated our methods based on three real-world datasets. Our experimental results show that our method can achieve a higher accuracy than other baseline methods such as the Gaussian Naive Bayes (GaussianNB) model, k-nearest neighbors (KNN) algorithm and Logistic Regression (LR) model.