Publications

2023

1. Communication-Efficient Personalized Federated Meta-Learning in Edge Networks

   Feng Yu , Hui Lin, Xiaoding Wang, Sahil Garg, Georges Kaddoum, Satinder Singh, and Mohammad Mehedi Hassan

   IEEE Transactions on Network and Service Management 2023

   Bib HTML

   @article{yu_communication-efficient_2023,
     title = {Communication-Efficient Personalized Federated Meta-Learning in Edge Networks},
     copyright = {4.758},
     issn = {1932-4537},
     doi = {10.1109/TNSM.2023.3263831},
     journal = {IEEE Transactions on Network and Service Management},
     author = {Yu, Feng and Lin, Hui and Wang, Xiaoding and Garg, Sahil and Kaddoum, Georges and Singh, Satinder and Hassan, Mohammad Mehedi},
     year = {2023},
     pages = {1--1},
     html = {https://doi.org/10.1109/tnsm.2023.3263831},
     bibtex_show = {true},
     abbr = {TNSM},
     selected = {true}
   }

2. An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems

   Ankita. Sharma, Shalli Rani, Syed Hassan Shah, Rohit Sharma, Feng Yu , and Mohammad Mehedi Hassan

   IEEE Transactions on Network Science and Engineering 2023

   Bib HTML

   Security is critical in the Cyber-Physical Systems (CPS) model for smart healthcare networks, and it will likely have a significant impact on the industry, medical and healthcare; and farming-related substructures shortly. Due to an increase in the frequency of security and privacy attacks in present times in healthcare networks, this article addressed a fundamental component of intrusion detection systems (IDS) based on the important parameter security. The limitations of IDS in reacting to cyberattacks as well as in establishing private controls in the field of smart healthcare have motivated this research. An efficient and lightweight deep learning-based CNN-Bidirectional LSTM is proposed for the DDoS detection that uses the features of Convolutional Neural Networks (CNNs) to classify traffic flows as benign and malicious in this study. The results are achieved using Python where four convolutional layers, Maximum Pooling, that ends with the Dense Layer. The hyperparameters used are batch size of 500, epochs 20, number of classes 25, and Relu and softmax pooling activation function along with the softmax

   @article{sharma_efficient_2023,
     title = {An Efficient Hybrid Deep Learning Model for Denial of Service Detection in Cyber Physical Systems},
     copyright = {5.033},
     issn = {2327-4697},
     doi = {10.1109/TNSE.2023.3273301},
     journal = {IEEE Transactions on Network Science and Engineering},
     author = {Sharma, Ankita. and Rani, Shalli and Shah, Syed Hassan and Sharma, Rohit and Yu, Feng and Hassan, Mohammad Mehedi},
     year = {2023},
     note = {Conference Name: IEEE Transactions on Network Science and Engineering},
     keywords = {Security, Internet of Things, Deep learning, Feature extraction, Deep Learning, Intrusion detection, Healthcare, Medical services, To-Read, /unread, CNN-LSTM, Cyber-Physical System, Cyber-physical systems, DDoS Attack},
     pages = {1--10},
     html = {https://doi.org/10.1109/TNSE.2023.3273301},
     bibtex_show = {true},
     abbr = {TNSE},
     selected = {false}
   }

2022

1. Blockchain-empowered secure federated learning system: Architecture and applications

   Feng Yu , Hui Lin, Xiaoding Wang, Abdussalam Yassine, and M. Shamim Hossain

   Computer Communications 2022

   Bib HTML PDF

   @article{YU202255,
     title = {Blockchain-empowered secure federated learning system: Architecture and applications},
     journal = {Computer Communications},
     volume = {196},
     pages = {55-65},
     year = {2022},
     issn = {0140-3664},
     doi = {https://doi.org/10.1016/j.comcom.2022.09.008},
     url = {https://www.sciencedirect.com/science/article/pii/S0140366422003474},
     author = {Yu, Feng and Lin, Hui and Wang, Xiaoding and Yassine, Abdussalam and Hossain, M. Shamim},
     html = {https://doi.org/10.1016/j.comcom.2022.09.008},
     pdf = {bfl_comcom2022.pdf},
     bibtex_show = {true},
     abbr = {COMCOM},
     selected = {true}
   }