Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks. (arXiv:2112.00988v2 [cs.LG] UPDATED)

Federated Learning (FL) has recently become an effective approach for
cyberattack detection systems, especially in Internet-of-Things (IoT) networks.
By distributing the learning process across IoT gateways, FL can improve
learning efficiency, reduce communication overheads and enhance privacy for
cyberattack detection systems. Challenges in implementation of FL in such
systems include unavailability of labeled data and dissimilarity of data
features in different IoT networks. In this paper, we propose a novel
collaborative learning framework that leverages Transfer Learning (TL) to
overcome …

arxiv collaborative cyberattack detection iot iot networks networks systems transfer transfer learning