An AreaContextBased Credibility Detection for Big Data in IoT In order to improve the credibility of big data analysis platformxe2x80x99s results in IoT, it is necessary to improve the quality of IoT data. Many detection methods have been proposed to filter out incredible data, but there are certain deficiencies that performance is not high, detection is not comprehensive, and process is not credible. So this paper proposes an areacontextbased credibility detection method for IoT data, which can effectively detect point anomalies, behavioral anomalies, and contextual anomalies. The performance of the context determination and the data credibility detection of the device satisfying the area characteristics is superior to the similar algorithms. As the experiments show, the proposed method can reach a high level of performance with more than 97% in metrics, which can effectively improve the quality of IoT data.

IDSAIoT: An Intrusion Detection System Architecture for IoT Networks The Internet of Things (IoT) allows large amounts and variety of devices to connect, interact and exchange data. The IoT network creates numerous opportunities for novel attacks that can compromise information and systems integrity. Intrusion detection systems have been studied over two decades, mostly employing traditional data mining and machine learning techniques that require an offline phase for model training on large amounts of data. This paper presents three data stream novelty detection techniques applied to the intrusion detection problem and proposes IDSAIoT, a novel implementation architecture, which combines the use of resources at the edge of the network and a public cloud. After an extensive empirical evaluation, results show that it is possible to identify new attack patterns soon after their emergence and to adapt the models in an efficient way.