Light-Weight Federated Transfer Learning Approach to Malware Detection on Computational Edges

With rapid increase in edge computing devices, Light weight methods to identify and stop cyber-attacks has become a topic of interest for the research community. Fast proliferation of smart devices and customer’s concerns regarding the data security and privacy has necessitated new methods to counter cyber attacks. This work presents a unique light weight transfer learning method to leverage malware detection in federated mode. Existing systems seems insufficient in terms of providing cyber security in resource constrained environment. Fast IoT device deployment raises a serious threat from malware attacks, which calls for more efficient, real-time detection systems. Using a transfer learning model over federated architecture (with federated learning support), the research suggests to counter the cyber risks and achieve efficiency in detection of malware in particular. Using a real-world publicly accessible IoT network dataset, the study assessed the performance of the model using Aposemat IoT-23 dataset. Extensive testing shows that with train-ing accuracy approaching around 98% and validation accuracy reaching 0.97.6% with 10 epoch, the proposed model achieves great detection accuracy of over 98%. These findings show how well the model detects Malware threats while keeping reasonable processing times—critical for IoT devices with limited resources.

@article{Mittal2025,
  title     = {Light-Weight Federated Transfer Learning Approach to Malware Detection on Computational Edges},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {2},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sakshi Mittal and Prateek Rajvanshi and Riaz Ul Amin},
  doi       = {10.14569/IJACSA.2025.0160202},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160202}
}