Edge‐based blockchain enabled anomaly detection for insider attack prevention in Internet of Things

Internet of Things (IoT) platforms are responsible for overall data processing in the IoT System. This ranges from analytics and big data processing to gathering all sensor data over time to analyze and produce long‐term trends. However, this comes with prohibitively high demand for resources such as memory, computing power and bandwidth, which the highly resource constrained IoT devices lack to send data to the platforms to achieve efficient operations. This results in poor availability and risk of data loss due to single point of failure should the cloud platforms suffer attacks. The integrity of the data can also be compromised by an insider, such as a malicious system administrator, without leaving traces of their actions. To address these issues, we propose in this work an edge‐based blockchain enabled anomaly detection technique to prevent insider attacks in IoT. The technique first employs the power of edge computing to reduce the latency and bandwidth requirements by taking processing closer to the IoT nodes, hence improving availability, and avoiding single point of failure. It then leverages some aspect of sequence‐based anomaly detection, while integrating distributed edge with blockchain that offers smart contracts to perform detection and correction of abnormalities in incoming sensor data. Evaluation of our technique using real IoT system datasets showed that the technique remarkably achieved the intended purpose, while ensuring integrity and availability of the data which is critical to IoT success. Different cloud services offer platforms for IoT big data analytics and processing, albeit at prohibitively high requirements for resources like memory and bandwidth, to constrained IoT devices. Employing edge computing helps lower some of those requirements, improve data availability and implement anomaly detection, but shares similar security issues as the cloud. As we presented, blockchain technology offers the prospects of ensuring IoT data integrity and was integrated with the edge to safeguard the data, which proved effective against insider attacks targeted at the system.