Editorial: Securing Internet of Things Through Big Data Analytics

Excerpt

The “IoT” heralds the connections of a nearly countless number of devices to the Internet thus promising accessibility, boundless scalability, amplified productivity and a surplus of additional paybacks [1]. The hype surrounding the IoT and its applications is already forcing companies to quickly upgrade their current processes, tools, and technology to accommodate massive data volumes and take advantage of insights. Since there is a vast amount of data generated by the IoT, a well-analyzed data is extremely valuable. However, the large-scale deployment of IoT will bring new challenges and IoT security is of them. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Continuously evolving models produce increasingly positive results, reducing the need for human interaction. These evolved models can be used to automatically produce reliable and repeatable decisions. Today’s machine learning algorithms comb through data sets that no human could feasibly get through in a year or even a lifetime’s worth of work. As the IoT continues to grow, more algorithms will be needed to keep up with the rising sums of data that accompany this growth. One of the main challenge of the IoT security is the integration with communication, computing, control, and physical environment parameters to analyze, detect and defend cyber-attacks in the distributed IoT systems. …