CIoTA: Collaborative IoT Anomaly Detection via Blockchain
This addresses security vulnerabilities in IoT devices for applications like smart homes or industrial systems, but it is incremental as it builds on existing blockchain and anomaly detection methods.
The paper tackles the problem of securing IoT devices from adversarial attacks during anomaly detection training by proposing CIoTA, a lightweight framework that uses blockchain for collaborative anomaly detection, and demonstrates its effectiveness in enhancing security on a simulation with 48 Raspberry Pis.
Due to their rapid growth and deployment, Internet of things (IoT) devices have become a central aspect of our daily lives. However, they tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such as anomaly detection, can help us secure the IoT devices. However, an anomaly detection model must be trained for a long time in order to capture all benign behaviors. This approach is vulnerable to adversarial attacks since all observations are assumed to be benign while training the anomaly detection model. In this paper, we propose CIoTA, a lightweight framework that utilizes the blockchain concept to perform distributed and collaborative anomaly detection for devices with limited resources. CIoTA uses blockchain to incrementally update a trusted anomaly detection model via self-attestation and consensus among IoT devices. We evaluate CIoTA on our own distributed IoT simulation platform, which consists of 48 Raspberry Pis, to demonstrate CIoTA's ability to enhance the security of each device and the security of the network as a whole.