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Verification and validation techniques for streaming big data analytics in internet of things environment

With an exponential growth of raw data generated from different sensors, actuators, and mobile devices, data analytics is becoming a challenging issue keeping in view of the heterogeneity of the data generated. The traditional database systems are not able to handle this huge amount of data. Current research and development are mainly focused on the analytics of this big data generated from different devices and overlook the difficulty of its secure streaming. In this study, the authors analyse and provide an overview on how to handle secure streaming data generated from different devices. It includes the major threats and risks, introduced during big data processing in Internet of things environment. Moreover, they analyse the existing security approaches and highlight emerging challenges required to process the heterogeneous big data in a secure manner for IoT applications, such as denial of service, malware, and phishing. The architectural details and security approaches required in each phase of big data processing life-cycle are explored in detail. Finally, various research challenges along with a case study are discussed and analysed.