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Article

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Title

Poisoning Attacks Against Communication and Computing Task Classification And Detection Techniques

Authors

[ 1 ] Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ 2 ] Instytut Radiokomunikacji, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ SzD ] doctoral school student | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2024

Published in

Sensors

Journal year: 2024 | Journal volume: vol. 24 | Journal number: iss. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • data poisoning
  • k-means algorithm
  • k-nearest neighbors algorithm
  • clustering
  • edge computing
Abstract

EN Machine learning-based classification algorithms allow communication and computing (2C) task offloading from the end devices to the edge computing network servers. In this paper, we consider task classification based on the hybrid k-means and k′ -nearest neighbors algorithms. Moreover, we examine the poisoning attacks on such ML algorithms, namely noise-like jamming and targeted data feature falsification, and their impact on the effectiveness of 2C task allocation. Then, we also present two anomaly detection methods using noise training and the silhouette score test to detect the poisoned samples and mitigate their impact. Our simulation results show that these attacks have a fatal effect on classification in feature areas where the decision boundary is unclear. They also demonstrate the effectiveness of our countermeasures against the considered attacks.

Pages (from - to)

338-1 - 338-19

DOI

10.3390/s24020338

URL

https://doi.org/10.3390/s24020338

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

3,4 [List 2023]

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