Due to its high traffic volume and real-time operation, a security framework is essential. The area of the Internet of Things is rapidly growing, raising severe security concerns to the entire network. For each area, we present experimental results supporting our approach and implementation. The third area is downsizing cryptography calculations, to fit IoT limitations without compromising security. The second area involves the implementation of the Random Forest algorithm to apply distributed and parallel processing for anomaly discovery. Hence, the detailed collected data from the sensors are no longer required for real-time anomaly detection. We collect historic data and analyze it using machine learning techniques to draw a contour, where all streaming values are expected to fall within the contour space. The first is the classification process required for ongoing anomaly detection, whereby values accepted or generated by a sensor are classified as valid or abnormal. ![]() In this chapter, we describe three areas, where we reduce the required storage space and processing power. Therefore, existing security systems are not applicable for IoT. To cope with it, we propose downsizing of existing security processes. ![]() Devices in the Internet of Things are very limited with processing capacity, memory and storage. These systems consume considerable computational resources. Standard security systems are widely implemented in the industry.
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