Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System

• URL: http://arxiv.org/abs/2208.08433v1
• Date: Wed, 17 Aug 2022 17:52:13 GMT
• Title: Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System
• Authors: Abdur R. Shahid, Ahmed Imteaj, Peter Y. Wu, Diane A. Igoche, and Tauhidul Alam
• Abstract summary: This paper presents the design of a label flipping data poisoning attack for a Human Activity Recognition (HAR) system. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications.
• Score: 0.5284812806199193
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 reading is maliciously changed in the data collection phase. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. Besides, vulnerability to label flipping attacks is dangerous when activity recognition models are deployed in safety-critical applications. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications. This is an earlier research work, to our knowledge, that explores attacking the HAR models via label flipping poisoning. We implement the proposed attack and test it on activity recognition models based on the following machine learning algorithms: multi-layer perceptron, decision tree, random forest, and XGBoost. Finally, we evaluate the effectiveness of K-nearest neighbors (KNN)-based defense mechanism against the proposed attack.

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