Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System Article Swipe
Abdur R. Shahid
,
Ahmed Imteaj
,
Peter Y. Wu
,
Diane Igoche
,
Tauhidul Alam
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.08433
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2208.08433
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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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2208.08433
- https://arxiv.org/pdf/2208.08433
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4300705397
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4300705397Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2208.08433Digital Object Identifier
- Title
-
Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition SystemWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-08-17Full publication date if available
- Authors
-
Abdur R. Shahid, Ahmed Imteaj, Peter Y. Wu, Diane Igoche, Tauhidul AlamList of authors in order
- Landing page
-
https://arxiv.org/abs/2208.08433Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2208.08433Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2208.08433Direct OA link when available
- Concepts
-
Computer science, Activity recognition, Machine learning, Artificial intelligence, Vulnerability (computing), Perceptron, Decision tree, Wearable computer, Random forest, Computer security, Artificial neural network, Embedded systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.misleading | 51 |
| abstract_inverted_index.poisoning. | 164 |
| abstract_inverted_index.(KNN)-based | 200 |
| abstract_inverted_index.Recognition | 2 |
| abstract_inverted_index.algorithms: | 183 |
| abstract_inverted_index.contaminate | 47 |
| abstract_inverted_index.maliciously | 82 |
| abstract_inverted_index.multi-layer | 184 |
| abstract_inverted_index.perceptron, | 185 |
| abstract_inverted_index.recognition | 108, 120, 175 |
| abstract_inverted_index.susceptible | 32 |
| abstract_inverted_index.uncertainty | 94 |
| abstract_inverted_index.environment, | 98 |
| abstract_inverted_index.interpreting | 8 |
| abstract_inverted_index.applications. | 126, 145 |
| abstract_inverted_index.effectiveness | 196 |
| abstract_inverted_index.vulnerability | 111 |
| abstract_inverted_index.safety-critical | 125 |
| abstract_inverted_index.smartphone-based | 141 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/4 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Quality Education |
| citation_normalized_percentile |