Adaptive and Robust Data Poisoning Detection and Sanitization in Wearable IoT Systems using Large Language Models

TL;DR

This paper introduces a novel LLM-based framework for detecting and sanitizing data poisoning in wearable IoT systems, enhancing robustness and adaptability without extensive dataset training.

Contribution

It presents a new approach using large language models with role play and reasoning techniques for real-time, dataset-efficient poisoning detection and sanitization in IoT systems.

Findings

High detection accuracy demonstrated

Effective sanitization with minimal latency

Reduced reliance on large labeled datasets

Abstract

The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve functionality and user experience. Although machine learning models have advanced HAR, they are increasingly susceptible to data poisoning attacks that compromise the data integrity and reliability of these systems. Conventional approaches to defending against such attacks often require extensive task-specific training with large, labeled datasets, which limits adaptability in dynamic IoT environments. This work proposes a novel framework that uses large language models (LLMs) to perform poisoning detection and sanitization in HAR systems, utilizing zero-shot, one-shot, and few-shot learning paradigms. Our approach incorporates \textit{role play} prompting, whereby the LLM assumes the role of expert to contextualize and evaluate sensor anomalies, and \textit{think step-by-step} reasoning, guiding the LLM to infer poisoning indicators in the raw sensor data and plausible clean alternatives. These strategies minimize reliance on curation of extensive datasets and enable robust, adaptable defense mechanisms in real-time. We perform an extensive evaluation of the framework, quantifying detection accuracy, sanitization quality, latency, and communication cost, thus demonstrating the practicality and effectiveness of LLMs in improving the security and reliability of wearable IoT systems.