No abstract

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

With the widespread propagation of Internet of Things through wireless sensor networks, massive amounts of sensor data are being generated at an unprecedented rate, resulting in very large quantities of explicit or implicit information. When analyzing such sensor data, it is of particular importance to detect accurately and efficiently not only individual anomalous behaviors but also anomalous events (i.e. patterns of behaviors). However, most previous work has focused only on detecting anomalies while generally ignoring the correlations between them. Even in approaches that take into account correlations between anomalies, most disregard the fact that the anomaly status of sensor data changes over time. In this article, we propose an unsupervised contextual anomaly detection method in Internet of Things through wireless sensor networks. This method accounts for both a dynamic anomaly status and correlations between anomalies based contextually on their spatial and temporal neighbors. We then demonstrate the effectiveness of the proposed method in an anomaly detection model. The experimental results show that this method can accurately and efficiently detect not only individual anomalies but also anomalous events.

As statistics show that most threats to information security in Internet of Things (IOT) are caused by data leakage, lots of methods have been developed to address the problem of data leakage prevention (DLP). However, most of these methods do not work well when the confidentiality of data changes frequently. We propose an Adaptive Feature Graph Update model (AFGU) to solve the problem by mapping the features of confidential data to the feature graph. First, the feature graph are built to record the features of confidential data which involve the sensitive terms and their context. Then, the improved evaluation method for the importance of each term is employed to update the feature graph according to the importance degree of each term. Finally, the confidentiality of data are determined by matching the features of the data with the feature graph. Experiments results show that the proposed method can detect confidential data effectively and efficiently.

The existing clustering algorithms based on grid are analyzed, and the clustering algorithms based on grid have the advantages of dealing with high dimensional data and high efficiency. However, traditional algorithms based on grid are influenced greatly by the granularity of grid partition. An incremental clustering algorithm based on grid, which is called IGrid, is proposed. IGrid has the advantage of high efficiency of traditional clustering algorithms based on grid, and it also partition the grid space by dimensional radius in a dynamic and incremental manner to improve the quality of clustering. The experiments on real datasets and synthetic datasets show that IGrid has better performance than traditional clustering algorithms based on grid in both speed and accuracy.