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Temporal Graph-Based Incident Analysis System for Internet of Things (ECML)

Read the Temporal Graph based Incident Analysis System for Internet of Things (ECML). Internet-of-things (IoTs) deploy massive number of sensors to monitor the system and environment. Anomaly detection on sensor data is an important task for IoT maintenance and operation. In real applications, the occurrence of a system-level incident usually involves hundreds of abnormal sensors, making it impractical for manual verification. The users require an efficient and effective tool to conduct incident analysis and provide critical information such as: (1) identifying the parts that suffered most damages and (2) finding out the ones that cause the incident. Unfortunately, existing methods are inadequate to fulfill these requirements because of the complex sensor relationship and latent anomaly influences in IoTs. To bridge the gap, we design and develop a Temporal Graph based Incident Analysis System (TGIAS) to help users’ diagnosis and reaction on reported anomalies. TGIAS trains a temporal graph to represent the anomaly relationship and computes severity ranking and causality score for each sensor. TGIAS provides the list of top k serious sensors and root-causes as output and illustrates the evidence on a graphical view. The system does not need any incident data for training and delivers high accurate analysis results in online time. TGIAS is equipped with a user-friendly interface, making it an effective tool for a broad range of IoTs.

Explainable Anomaly Detection System for Categorical Sensor Data in Internet of Things

Explainable Anomaly Detection System for Categorical Sensor Data in Internet of Things Internet of things (IoT) applications deploy massive number of sensors to monitor the system and environment. Anomaly detection on streaming sensor data is an important task for IoT maintenance and operation. However, there are two major challenges for anomaly detection in real IoT applications: (1) many sensors report categorical values rather than numerical readings, (2) the end users may not understand the detection results, they require additional knowledge and explanations to make decision and take action. Unfortunately, most existing solutions cannot satisfy such requirements. To bridge the gap, we design and develop an eXplainable Anomaly Detection System (XADS) for categorical sensor data. XADS trains models from historical normal data and conducts online monitoring. XADS detects the anomalies in an explainable way: the system not only reports anomalies’ time periods, types, and detailed information, but also provides explanations on why they are abnormal, and what the normal data look like. Such information significantly helps the decision making for users. Moreover, XADS requires limited parameter setting in advance, yields high accuracy on detection results and comes with a user-friendly interface, making it an efficient and effective tool to monitor a wide variety of IoT applications.

3D Histogram based Anomaly Detection for Categorical Sensor Data in Internet of Things

3D Histogram based Anomaly Detection for Categorical Sensor Data in Internet of Things The applications of Internet-of-things (IoT) deploy massive number of sensors to monitor the system and environment. Anomaly detection on streaming sensor data is an important task for IoT maintenance and operation. In real IoT applications, many sensors report categorical values rather than numerical readings. Unfortunately, most existing anomaly detection methods are designed only for numerical sensor data. They cannot be used to monitor the categorical sensor data. In this study, we design and develop a 3D Histogram based Categorical Anomaly Detection (HCAD) solution to monitor categorical sensor data in IoT. HCAD constructs the histogram model by three dimensions: categorical value, event duration, and frequency. The histogram models are used to profile normal working states of IoT devices. HCAD automatically determines the range of normal data and anomaly threshold. It only requires very limit parameter setting and can be applied to a wide variety of different IoT devices. We implement HCAD and integrate it into an online monitoring system. We test the proposed solution on real IoT datasets such as telemetry data from satellite sensors, air quality data from chemical sensors, and transportation data from traffic sensors. The results of extensive experiments show that HCAD achieves higher detecting accuracy and efficiency than state-of-the-art methods.