Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System. (arXiv:2208.08433v1 [cs.CR])

Human Activity Recognition (HAR) is a problem of interpreting sensor data to
human movement using an efficient machine learning (ML) approach. The HAR
systems rely on data from untrusted users, making them susceptible to data
poisoning attacks. In a poisoning attack, attackers manipulate the sensor
readings to contaminate the training set, misleading the HAR to produce
erroneous outcomes. This paper presents the design of a label flipping data
poisoning attack for a HAR system, where the label of a sensor …

attack data data poisoning human poisoning recognition system wearable