# Dynamic Black-box Backdoor Attacks on IoT Sensory Data

arXiv: https://arxiv.org/abs/2511.14074 · HTML: https://mlsanity.com/paper/2511.14074/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 17 references that are in the map.
- First posted on arXiv 18 Nov 2025.

## Builds on (most cited first, 11 of 11)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks](https://mlsanity.com/paper/1803.03635/) (2018)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Detecting Backdoor Attacks on Deep Neural Networks by Activation Clustering](https://mlsanity.com/paper/1811.03728/) (2018)
- [Input-Aware Dynamic Backdoor Attack](https://mlsanity.com/paper/2010.08138/) (2020)
- [A General Framework for Adversarial Examples with Objectives](https://mlsanity.com/paper/1801.00349/) (2017)
- [Mobile Sensor Data Anonymization](https://mlsanity.com/paper/1810.11546/) (2018)
- [Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey](https://mlsanity.com/paper/2302.02515/) (2023)
- [UniMiB SHAR: a new dataset for human activity recognition using acceleration data from smartphones](https://mlsanity.com/paper/1611.07688/) (2016)
- [Protecting Sensory Data against Sensitive Inferences](https://mlsanity.com/paper/1802.07802/) (2018)
- [Replacement AutoEncoder: A Privacy-Preserving Algorithm for Sensory Data Analysis](https://mlsanity.com/paper/1710.06564/) (2017)

## Built on by (most cited first, 8 of 8)

- [Contrastive Continual Learning for Model Adaptability in Internet of Things](https://mlsanity.com/paper/2602.04881/) (2026)
- [Backdoor Attacks on Multi-modal Contrastive Learning](https://mlsanity.com/paper/2601.11006/) (2026)
- [Contrastive Learning for Privacy Enhancements in Industrial Internet of Things](https://mlsanity.com/paper/2602.00515/) (2026)
- [Backdoor Attacks on Contrastive Continual Learning for IoT Systems](https://mlsanity.com/paper/2602.13062/) (2026)
- [CFD-HAR: User-controllable Privacy through Conditional Feature Disentanglement](https://mlsanity.com/paper/2603.11526/) (2026)
- [Backdoor Attacks on Fault Detection and Localization in Cyber-Physical Systems](https://mlsanity.com/paper/2605.27674/) (2026)
- [Continual Backdoor Training in IoT/CPS](https://mlsanity.com/paper/2606.14987/) (2026)
- [CLIP-guided Diffusion Model for Backdoor Generation in Sensor-based Human Activity Recognition](https://mlsanity.com/paper/2606.22837/) (2026)

## Abstract (arXiv)

Sensor data-based recognition systems are widely used in various applications, such as gait-based authentication and human activity recognition (HAR). Modern wearable and smart devices feature various built-in Inertial Measurement Unit (IMU) sensors, and such sensor-based measurements can be fed to a machine learning-based model to train and classify human activities. While deep learning-based models have proven successful in classifying human activity and gestures, they pose various security risks. In our paper, we discuss a novel dynamic trigger-generation technique for performing black-box adversarial attacks on sensor data-based IoT systems. Our empirical analysis shows that the attack is successful on various datasets and classifier models with minimal perturbation on the input data. We also provide a detailed comparative analysis of performance and stealthiness to various other poisoning techniques found in backdoor attacks. We also discuss some adversarial defense mechanisms and their impact on the effectiveness of our trigger-generation technique.