Výsledky vyhledávání - "Husnoo, Muhammad Akbar"

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Decentralized Federated Anomaly Detection in Smart Grids: A P2P Gossip Approach

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Haque, Md Enamul, Mahmood, A. N.

The increasing security and privacy concerns in the Smart Grid sector have led to a significant demand for robust intrusion detection systems within critical smart grid infrastructure. To address the challenges posed by privacy preservation and decen

Externí odkaz: http://arxiv.org/abs/2407.15879

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

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Reda, Haftu Tasew, Hosseinzadeh, Nasser, Islam, Shama Naz, Mahmood, Abdun Naser, Doss, Robin

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 the top-priority cyber-related issues and have received significant atte

Externí odkaz: http://arxiv.org/abs/2304.03640

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

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Reda, Haftu Tasew, Hosseizadeh, Nasser, Islam, Shama Naz, Mahmood, Abdun Naser, Doss, Robin

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

Externí odkaz: http://arxiv.org/abs/2303.16956

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A Secure Federated Learning Framework for Residential Short Term Load Forecasting

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Hosseinzadeh, Nasser, Islam, Shama Naz, Mahmood, Abdun Naser, Doss, Robin

Smart meter measurements, though critical for accurate demand forecasting, face several drawbacks including consumers' privacy, data breach issues, to name a few. Recent literature has explored Federated Learning (FL) as a promising privacy-preservin

Externí odkaz: http://arxiv.org/abs/2209.14547

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FedREP: Towards Horizontal Federated Load Forecasting for Retail Energy Providers

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Hosseinzadeh, Nasser, Islam, Shama Naz, Mahmood, Abdun Naser, Doss, Robin

As Smart Meters are collecting and transmitting household energy consumption data to Retail Energy Providers (REP), the main challenge is to ensure the effective use of fine-grained consumer data while ensuring data privacy. In this manuscript, we ta

Externí odkaz: http://arxiv.org/abs/2203.00219

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False Data Injection Threats in Active Distribution Systems: A Comprehensive Survey

Autor: Husnoo, Muhammad Akbar, Anwar, Adnan, Hosseinzadeh, Nasser, Islam, Shama Naz, Mahmood, Abdun Naser, Doss, Robin

With the proliferation of smart devices and revolutions in communications, electrical distribution systems are gradually shifting from passive, manually-operated and inflexible ones, to a massively interconnected cyber-physical smart grid to address

Externí odkaz: http://arxiv.org/abs/2111.14251

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