Context-Aware Spatiotemporal Poisoning Attacks on Wearable-Based Activity Recognition

The rapid progress in wearable sensors, smartphones equipped with sensors, and seamless cloud integration has ignited significant research into the creation of IoT-driven intelligent systems designed for human activity recognition (HAR). However, the reliance on external data curation for training machine learning (ML)- based recognition models renders the system susceptible to adversarial attacks. The limited existing research in this area calls for extensive efforts to address its gaps, notably the absence of vital contextual information crucial for HAR systems. In this poster, we present our ongoing research effort on investigating context-aware spatiotemporal data poisoning attacks on the intelligence of HAR systems. These attacks involve attackers exploiting specific spatiotemporal patterns and conditions to manipulate the labels of activity data. This manipulation is strategically designed to mislead the recognition system, thereby highlighting the pressing need for improved security measures in this rapidly evolving domain.