TY  - JOUR
AU  - Myakala, Praveen Kumar
AU  - Kamatala, Srikanth
AU  - Bura, Chiranjeevi
PY  - 2025
DA  - 2025/05/20
TI  - Privacy-Preserving Federated Learning for IoT Botnet Detection: A Federated Averaging Approach
JO  - ICCK Transactions on Machine Intelligence
T2  - ICCK Transactions on Machine Intelligence
JF  - ICCK Transactions on Machine Intelligence
VL  - 1
IS  - 1
SP  - 6
EP  - 16
DO  - 10.62762/TMI.2025.796490
UR  - https://www.icck.org/article/abs/TMI.2025.796490
KW  - federated learning
KW  - federated averaging (FedAvg)
KW  - privacy-preserving machine learning
KW  - IoT Security
KW  - botnet detection
KW  - edge AI
AB  - Traditional centralized machine learning approaches for IoT botnet detection pose significant privacy risks, as they require transmitting sensitive device data to a central server. This study presents a privacy-preserving Federated Learning (FL) approach that employs Federated Averaging (FedAvg) to detect prevalent botnet attacks, such as Mirai and Gafgyt, while ensuring that raw data remain on local IoT devices. Using the N-BaIoT dataset, which contains real-world benign and malicious traffic, we evaluated both the IID and non-IID data distributions to assess the effects of decentralized training. Our approach achieved 97.5% accuracy in IID and 95.2% in highly skewed non-IID scenarios, closely matching centralized learning performance while preserving privacy. Additionally, communication optimization techniques—Top-20% gradient sparsification and 8-bit quantization—reduce communication overhead by up to 80%, significantly enhancing the efficiency. Our convergence analysis further shows that FedAvg remains effective under non-IID conditions, thereby demonstrating its robustness for real-world deployments. These results demonstrate that FL provides a scalable and privacy-preserving solution for securing IoT networks against botnet threats.
SN  - 3068-7403
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Myakala2025PrivacyPre,
  author = {Praveen Kumar Myakala and Srikanth Kamatala and Chiranjeevi Bura},
  title = {Privacy-Preserving Federated Learning for IoT Botnet Detection: A Federated Averaging Approach},
  journal = {ICCK Transactions on Machine Intelligence},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {6-16},
  doi = {10.62762/TMI.2025.796490},
  url = {https://www.icck.org/article/abs/TMI.2025.796490},
  abstract = {Traditional centralized machine learning approaches for IoT botnet detection pose significant privacy risks, as they require transmitting sensitive device data to a central server. This study presents a privacy-preserving Federated Learning (FL) approach that employs Federated Averaging (FedAvg) to detect prevalent botnet attacks, such as Mirai and Gafgyt, while ensuring that raw data remain on local IoT devices. Using the N-BaIoT dataset, which contains real-world benign and malicious traffic, we evaluated both the IID and non-IID data distributions to assess the effects of decentralized training. Our approach achieved 97.5\% accuracy in IID and 95.2\% in highly skewed non-IID scenarios, closely matching centralized learning performance while preserving privacy. Additionally, communication optimization techniques—Top-20\% gradient sparsification and 8-bit quantization—reduce communication overhead by up to 80\%, significantly enhancing the efficiency. Our convergence analysis further shows that FedAvg remains effective under non-IID conditions, thereby demonstrating its robustness for real-world deployments. These results demonstrate that FL provides a scalable and privacy-preserving solution for securing IoT networks against botnet threats.},
  keywords = {federated learning, federated averaging (FedAvg), privacy-preserving machine learning, IoT Security, botnet detection, edge AI},
  issn = {3068-7403},
  publisher = {Institute of Central Computation and Knowledge}
}