Assessing the Effect of Model Poisoning Attacks on Federated Learning in Android Malware Detection-Reference-Cited by-同舟云学术

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Assessing the Effect of Model Poisoning Attacks on Federated Learning in Android Malware Detection

Published:2024-05-25 Issue: Volume:34 Page:147-154
ISSN:
Container-title:Proceedings of the Cognitive Models and Artificial Intelligence Conference
language:
Short-container-title:

Author:

Nawshin Faria¹^ORCID,Arnal Romain²^ORCID,Unal Devrim³^ORCID,Suganthan Ponnuthurai³^ORCID,Touseau Lionel⁴^ORCID

Affiliation:

1. KINDI Computing Research Center, College of Engineering, Qatar University, Qatar and Department of Computer Science & Engineering, College of Engineering, Qatar University, Qatar

2. Saint-Cyr Coetquidan Military Academy, France

3. KINDI Computing Research Center, College of Engineering, Qatar University, Qatar

4. Saint-Cyr Coetquidan Military Academy, CReC Saint-Cyr, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3660853.3660887

Reference25 articles.

1. A privacy-preserving federated learning with a secure collaborative for malware detection models using Internet of Things resources

2. Federated Learning: A Survey on Enabling Technologies, Protocols, and Applications

3. AndroZoo. 2023. AndroZoo. https://androzoo.uni.lu/. Accessed on: 2024-01-05.

4. MPAF: Model Poisoning Attacks to Federated Learning based on Fake Clients

5. SMOTE: Synthetic Minority Over-sampling Technique

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