HSTCN: A Semisupervised Hierarchical Stacking Temporal Convolutional Network for Anomaly Detection in IoT The rapid development of Internet of Things (IoT) accumulates lots of communication data. Not being processed in time, these massive data increase the difficulty of anomaly detection for smart service. Furthermore, labeling all communication data is unpractical. Therefore, it is necessary to accomplish some feasible ways which can incorporate unlabeled data effectively. In recent years, Temporal Convolutional Network (TCN) has been proposed to solve sequence problems and got better performance compared to Recurrent Neural Network (RNN) in most cases. This paper proposes a semisupervised hierarchical stacking TCN for the first time, which concentrates on the anomaly detection of the communication in smart home. The ideology of hierarchical model fully considers the features of streaming data in smart home scenario, and the stacking method is used to weed out the outliers. In this way, the detection accuracy can be highly improved. Finally, experimental results demonstrate that the proposed model can promote the security of the communication in smart home with a large extent as well as get much better performance.

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.