Combating False Data Injection Attacks on Human-Centric Sensing Applications

Code for the ACM IMWUT 2022 paper Combating False Data Injection Attacks on Human-Centric Sensing Applications. This repository contains model training and testing code on three datasets mentioned in the paper.

Original Datasets

BB-MAS: https://ieee-dataport.org/open-access/su-ais-bb-mas-syracuse-university-and-assured-information-security-behavioral-biometrics

WISDM: http://archive.ics.uci.edu/ml/datasets/WISDM+Smartphone+and+Smartwatch+Activity+and+Biometrics+Dataset+

WESAD: https://archive.ics.uci.edu/ml/datasets/WESAD+%28Wearable+Stress+and+Affect+Detection%29

Citation

If this paper can help with your research, please cite us at:

@article{xin2022combating,
  title={Combating False Data Injection Attacks on Human-Centric Sensing Applications},
  author={Xin, Jingyu and Phoha, Vir V and Salekin, Asif},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={6},
  number={2},
  pages={1--22},
  year={2022},
  publisher={ACM New York, NY, USA}
}

Contact

For any questions, please email at jxin05@syr.edu