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.

由于其快速增长和部署，物联网（IoT）已成为我们日常生活的中心方面。不幸的是，物联网设备往往具有许多可被攻击者利用的漏洞。可以使用无监督技术（例如异常检测）以即插即用的方式保护这些设备。但是，异常检测模型必须经过长时间训练才能捕获所有良性行为。此外，由于在训练阶段所有假设均被认为是良性的，因此异常检测模型容易受到对抗攻击。在本文中，我们提出（1）一种用于异常检测的新颖方法，以及（2）一种轻量级框架，该框架利用区块链在分布式环境中整合异常检测模型。区块链框架通过自我证明和物联网设备之间的共识，逐步更新可信赖的异常检测模型。我们在由48个Raspberry Pi组成的分布式IoT仿真平台上评估我们的方法。仿真演示了该方法如何增强每个设备的安全性以及整个网络的安全性。