Secure and Efficient Federated Learning in LEO Constellations using Decentralized Key Generation and On-Orbit Model Aggregation

no code implementations • 4 Sep 2023 • Mohamed Elmahallawy, Tie Luo, Mohamed I. Ibrahem

Our analysis and results show that FedSecure preserves the privacy of each satellite's data against eavesdroppers, a curious server, or curious satellites.

Federated Learning Privacy Preserving

Real-Time Detection of False Readings in Smart Grid AMI Using Deep and Ensemble Learning research model code github

no code implementations • IEEE Access 2022 • MOHAMMED J. ABDULAAL, Mohamed I. Ibrahem, MOHAMED M. E. A. MAHMOUD, JUNAID KHALID, ABDULAH JEZA ALJOHANI, AHMAD H. MILYANI, AND ABDULLAH M. ABUSORRAH

The existing solutions in the literature focus only on securing the billing, so they are not designed to detect the attacks in real time, and thus the SO may use false readings for a long period of time in load monitoring and energy management until they are identified.

energy management Ensemble Learning +1

Detection of False-Reading Attacks in the AMI Net-Metering System

no code implementations • 2 Dec 2020 • Mahmoud M. Badr, Mohamed I. Ibrahem, Mohamed Mahmoud, Mostafa M. Fouda, Waleed Alasmary

Based on the data analysis, we propose a general multi-data-source deep hybrid learning-based detector to identify the false-reading attacks.

energy management Management