Recording runtime status via logs is common for almost every computer system, and detecting anomalies in logs is crucial for timely identifying malfunctions of systems. However, manually detecting anomalies for logs is time-consuming, error-prone, and infeasible. Existing automatic log anomaly detection approaches, using indexes rather than semantics of log templates, tend to cause false alarms. In this work, we propose LogAnomaly, a framework to model unstructured a log stream as a natural language sequence. Empowered by template2vec, a novel, simple yet effective method to extract the semantic information hidden in log templates, LogAnomaly can detect both sequential and quantitive log anomalies simultaneously, which were not done by any previous work. Moreover, LogAnomaly can avoid the false alarms caused by the newly appearing log templates between periodic model retrainings. Our evaluation on two public production log datasets show that LogAnomaly outperforms existing log-based anomaly detection methods.

Outliers may cause model deviation and then affect the monitoring performance and hence it is a challenging problem for process monitoring. The robust principal component analysis (RPCA) approach, which describes outlier components with a sparse matrix and identifies these components using the sparse matrix recovery approach, is the most commonly used method to solve the model deviation problems caused by outliers. However, because the existing mathematical tools can only obtain a nonsparse matrix with small element values, RPCA performs poorly during process monitoring. In this paper, we propose a novel robust PCA scheme called moment-based RPCA (MRPCA). In the offline training stage, MRPCA adopts a novel outlier selection mechanism based on the difference between the higher-order and second-order central moments to select outlier samples; in the online monitoring stage, MRPCA adopts an outlier detection mechanism to distinguish outliers from fault data. Using the aforementioned mechanisms, MRPCA achieves high fault detection and low false alarm rates in tests of a numerical model and the Tennessee Eastman process.

In this paper, a new sparse approximation technique is proposed for incremental power grid analysis. Our proposed method is motivated by the observation that when a power grid network is locally updated during circuit design, its response changes locally and, hence, the incremental "change" of the power grid voltage is almost zero at many internal nodes, resulting in a unique sparse pattern. An efficient Orthogonal Matching Pursuit (OMP) algorithm is adopted to solve the proposed sparse approximation problem. In addition, several numerical techniques are proposed to improve the numerical stability of the proposed solver, while simultaneously maintaining its high efficiency. Several industrial circuit examples demonstrate that when applied to incremental power grid analysis, our proposed approach achieves up to 130u runtime speed-up over the traditional Algebraic Multi-Grid (AMG) method, without surrendering any accuracy.

No abstract

The linear quadratic Gaussian (LQG) control for a quadrotor unmanned aerial vehicle (UAV) under false data injection attacks is studied. The LQG control depends on optimal state estimation, while this type of attacks makes the optimal state estimation unimplementable in practice. To address this problem, the authors prose a framework to detect attacks and then augment the information for controller design. Based on this framework, the LQG control is designed and a sufficient condition for the security of the closed‐loop control system is given. Finally, the LQG controller is applied to a quadrotor UAV, and some experiments are carried out to illustrate its effectiveness.