Scalable and Robust Outlier Detector using Hierarchical Clustering and Long ShortTerm Memory (LSTM) Neural Network for the Internet of Things Abstract   The emerging centralized entities, like cloud, edge, or SoftwareDefined Network (SDN), make automated decisions for the Internet of Things (IoT) applications based on the measured data from several sensors. However, the malicious injection of the anomalies or outliers in measured sensor data may disrupt the automated decision making capabilities of the applications running at the centralized location. Therefore, the detection of such outliers is an essential problem for IoT that needs to be researched out. This paper presents a scalable outlier detector that uses Hierarchical clustering in conjunction with Long ShortTerm Memory (LSTM) neural network. Hierarchical clustering provides scalability to the outlier detector by finding correlated sensors. The LSTM neural network is coupled with the robust statistics, Mestimator, to accurately detect outliers in timeseries data. The simulation results on different datasets show that the proposed method has an accuracy of more than 90% for different attack strength. Also, the model parameter can be tuned according to the application requirement so that the outlier detector can be tailored to either precision or recall sensitive.

Unsupervised Online Anomaly Detection on Multivariate Sensing Time Series Data for Smart Manufacturing The emergence of IoT and AI has brought revolutionary change in various application domains. One of them is Industry 4.0, also called Smart Manufacturing, which aims to achieve highly flexible and automated production processes. In this paper, we study a use case of anomaly detection in smart manufacturing using the real data collected from the sensing devices of a factory production line. Our goal is to improve the anomaly detection accuracy at an earlier stage of production line, so that cost and time wasted by possible production failures can be reduced. To overcome the limited and irregular anomaly patterns found from our multivariate sensor dataset, we proposed an unsupervised realtime anomaly detection algorithm based on LSTMbased AutoEncoder. Our evaluations show that our approach achieved almost 90% accuracy for both precision and recall while other classification or regression based methods only reached 70%85%.