all InfoSec news
FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination. (arXiv:2303.16956v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
With growing security and privacy concerns in the Smart Grid domain,
intrusion detection on critical energy infrastructure has become a high
priority in recent years. To remedy the challenges of privacy preservation and
decentralized power zones with strategic data owners, Federated Learning (FL)
has contemporarily surfaced as a viable privacy-preserving alternative which
enables collaborative training of attack detection models without requiring the
sharing of raw data. To address some of the technical challenges associated
with conventional synchronous FL, this paper …
address asynchronous attack challenges critical cyberattack data decentralized detection discrimination domain energy energy infrastructure federated learning framework grid high infrastructure intrusion intrusion detection novel power preservation privacy remedy security sharing smart strategic system technical training