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20192022
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cs.CR2023

FedDiSC: A Computation-efficient Federated Learning Framework for Power Systems Disturbance and Cyber Attack Discrimination

Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda +4

With the growing concern about the security and privacy of smart grid systems, cyberattacks on critical power grid components, such as state estimation, have proven to be one of th…

cs.CR20231 cited

FeDiSa: A Semi-asynchronous Federated Learning Framework for Power System Fault and Cyberattack Discrimination

Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda +4

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…

cs.CR2022

Towards Privacy-Preserving Neural Architecture Search

Fuyi Wang, Leo Yu Zhang, Lei Pan +2

Machine learning promotes the continuous development of signal processing in various fields, including network traffic monitoring, EEG classification, face identification, and many…

cs.CR2020

Towards Decentralized IoT Updates Delivery Leveraging Blockchain and Zero-Knowledge Proofs

Edoardo Puggioni, Arash Shaghaghi, Robin Doss +1

We propose CrowdPatching, a blockchain-based decentralized protocol, allowing Internet of Things (IoT) manufacturers to delegate the delivery of software updates to self-interested…

cs.CR2019

Security Analysis Methods on Ethereum Smart Contract Vulnerabilities: A Survey

Purathani Praitheeshan, Lei Pan, Jiangshan Yu +2

Smart contracts are software programs featuring both traditional applications and distributed data storage on blockchains. Ethereum is a prominent blockchain platform with the supp…