  Due to their rapid growth and deployment, the Internet of things (IoT) have
become a central aspect of our daily lives. Unfortunately, IoT devices tend to
have many vulnerabilities which can be exploited by an attacker. Unsupervised
techniques, such as anomaly detection, can be used to secure these devices in a
plug-and-protect manner.
  However, anomaly detection models must be trained for a long time in order to
capture all benign behaviors. Furthermore, the anomaly detection model is
vulnerable to adversarial attacks since, during the training phase, all
observations are assumed to be benign. In this paper, we propose (1) a novel
approach for anomaly detection and (2) a lightweight framework that utilizes
the blockchain to ensemble an anomaly detection model in a distributed
environment.
  Blockchain framework incrementally updates a trusted anomaly detection model
via self-attestation and consensus among the IoT devices. We evaluate our
method on a distributed IoT simulation platform, which consists of 48 Raspberry
Pis. The simulation demonstrates how the approach can enhance the security of
each device and the security of the network as a whole.
