Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System
Author:
Affiliation:
1. Robert Morris University,Department of Computer and Information Systems,Moon,PA,USA
2. Southern Illinois University,School of Computing,Carbondale,IL,USA
3. Louisiana State University Shreveport,Department of Computer Science,Shreveport,LA,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10022049/10022014/10022015.pdf?arnumber=10022015
Reference26 articles.
1. Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review
2. Exploiting Federated Learning Technique to Recognize Human Activities in Resource-Constrained Environment
3. A Military Human Performance Management System Design using Machine Learning Algorithms
4. No Soldiers Left Behind: An IoT-Based Low-Power Military Mobile Health System Design
5. Multi-scale deep feature learning for human activity recognition using wearable sensors;tang;IEEE Transactions on Industrial Electronics,2022
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1. Securing Contrastive mmWave-based Human Activity Recognition against Adversarial Label Flipping;Proceedings of the 17th ACM Conference on Security and Privacy in Wireless and Mobile Networks;2024-05-27
2. Context-Aware Spatiotemporal Poisoning Attacks on Wearable-Based Activity Recognition;IEEE INFOCOM 2024 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS);2024-05-20
3. A Holistic Review of Machine Learning Adversarial Attacks in IoT Networks;Future Internet;2024-01-19
4. Study on Poisoning Attacks: Application Through an IoT Temperature Dataset;2023 IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE);2023-12-14
5. On the Robustness of Random Forest Against Untargeted Data Poisoning: An Ensemble-Based Approach;IEEE Transactions on Sustainable Computing;2023-10
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