Wang, Ruixuan, Kong, Fanxin, Sudler, Hasshi, and Jiao, Xun. Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks. Retrieved from https://par.nsf.gov/biblio/10294492. 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS) . Web. doi:10.1109/RTAS52030.2021.00052.

Wang, Ruixuan, Kong, Fanxin, Sudler, Hasshi, & Jiao, Xun. Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks. 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS), (). Retrieved from https://par.nsf.gov/biblio/10294492. https://doi.org/10.1109/RTAS52030.2021.00052

Wang, Ruixuan, Kong, Fanxin, Sudler, Hasshi, and Jiao, Xun. "Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks". 2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS) (). Country unknown/Code not available. https://doi.org/10.1109/RTAS52030.2021.00052. https://par.nsf.gov/biblio/10294492.

@article{osti_10294492,
place = {Country unknown/Code not available}, title = {Brief Industry Paper: HDAD: Hyperdimensional Computing-based Anomaly Detection for Automotive Sensor Attacks}, url = {https://par.nsf.gov/biblio/10294492}, DOI = {10.1109/RTAS52030.2021.00052}, abstractNote = {}, journal = {2021 IEEE 27th Real-Time and Embedded Technology and Applications Symposium (RTAS)}, author = {Wang, Ruixuan and Kong, Fanxin and Sudler, Hasshi and Jiao, Xun}, editor = {null} }