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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Federated Deep Learning For Cyber Security In The Internet Of Things: Concepts, Applications, And Experimental Analysis, Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu
Federated Deep Learning For Cyber Security In The Internet Of Things: Concepts, Applications, And Experimental Analysis, Mohamed Amine Ferrag, Othmane Friha, Leandros Maglaras, Helge Janicke, Lei Shu
Research outputs 2014 to 2021
In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated …
A Novel Augmented Deep Transfer Learning For Classification Of Covid-19 And Other Thoracic Diseases From X-Rays, Fouzia Atlaf, Syed M. S. Islam, Naeem K. Janjua
A Novel Augmented Deep Transfer Learning For Classification Of Covid-19 And Other Thoracic Diseases From X-Rays, Fouzia Atlaf, Syed M. S. Islam, Naeem K. Janjua
Research outputs 2014 to 2021
Deep learning has provided numerous breakthroughs in natural imaging tasks. However, its successful application to medical images is severely handicapped with the limited amount of annotated training data. Transfer learning is commonly adopted for the medical imaging tasks. However, a large covariant shift between the source domain of natural images and target domain of medical images results in poor transfer learning. Moreover, scarcity of annotated data for the medical imaging tasks causes further problems for effective transfer learning. To address these problems, we develop an augmented ensemble transfer learning technique that leads to significant performance gain over the conventional transfer …