English

CIoTA: Collaborative IoT Anomaly Detection via Blockchain

Computers and Society 2018-04-11 v2 Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning

Abstract

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.

Keywords

Cite

@article{arxiv.1803.03807,
  title  = {CIoTA: Collaborative IoT Anomaly Detection via Blockchain},
  author = {Tomer Golomb and Yisroel Mirsky and Yuval Elovici},
  journal= {arXiv preprint arXiv:1803.03807},
  year   = {2018}
}

Comments

Appears in the workshop on Decentralized IoT Security and Standards (DISS) of the Network and Distributed Systems Security Symposium (NDSS) 2018

R2 v1 2026-06-23T00:48:29.038Z