Lightweight Collaborative Anomaly Detection for the IoT using Blockchain

TL;DR

This paper introduces a lightweight, blockchain-based framework for collaborative anomaly detection in IoT networks, enabling distributed model updates and improved security against vulnerabilities and adversarial attacks.

Contribution

It presents a novel blockchain-enabled approach for incremental, distributed anomaly detection model updates in IoT environments, enhancing security and robustness.

Findings

Effective anomaly detection on IoT devices using blockchain

Distributed model updates improve security resilience

Validated on a 48 Raspberry Pi IoT simulation platform

Abstract

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.