CIoTA: Collaborative IoT Anomaly Detection via Blockchain

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

6 Downloads (Pure)

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.
Original languageEnglish
Title of host publicationWorkshop on Decentralized IoT Security and Standards (DISS) located with the Network and Distributed System Security Symposium (NDSS)
ISBN (Electronic) 1-891562-51-7
DOIs
StatePublished - 2018
EventWorkshop on Decentralized IoT Security and Standards (DISS), located with the Network and Distributed System Security Symposium (NDSS) - San Diego, CA, United States
Duration: 18 Feb 201821 Feb 2018

Conference

ConferenceWorkshop on Decentralized IoT Security and Standards (DISS), located with the Network and Distributed System Security Symposium (NDSS)
Country/TerritoryUnited States
Period18/02/1821/02/18

Keywords

  • cs.CY
  • cs.CR
  • cs.DC
  • cs.LG

Fingerprint

Dive into the research topics of 'CIoTA: Collaborative IoT Anomaly Detection via Blockchain'. Together they form a unique fingerprint.

Cite this