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RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data ...
Privacy-Preserving Distributed IDS Using Incremental Learning for IoT Health Systems
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
Existing techniques for incremental learning are computationally expensive and produce duplicate features leading to higher false positive and true negative rates. We propose a novel privacy-preserving intrusion detection ...
Weighted Trustworthiness for ML Based Attacks Classification
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference Paper)
Recently, machine learning techniques are gaining a lot of interest in security applications as they exhibit fast processing with real-time predictions. One of the significant challenges in the implementation of these ...
Energy-efficient networks selection based deep reinforcement learning for heterogeneous health systems
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
Smart health systems improve the existing health services by integrating information and technology into health and medical practices. However, smart healthcare systems are facing major challenges including limited network ...
QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach
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Institute of Electrical and Electronics Engineers Inc.
, 2019 , Conference Paper)
Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers ...
Hierarchical Federated Learning for Collaborative IDS in IoT Applications
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference Paper)
As the Internet-of-Things devices are being very widely adopted in all fields, such as smart houses, healthcare, and transportation, extremely huge amounts of data are being gathered, shared, and processed. This fact raises ...
Deep Reinforcement Learning for Network Selection over Heterogeneous Health Systems
(
IEEE Computer Society
, 2022 , Article)
Smart health systems improve our quality oflife by integrating diverse information and technologies into health and medical practices. Such technologies can significantly improve the existing health services. However, ...
Optimal User-Edge Assignment in Hierarchical Federated Learning Based on Statistical Properties and Network Topology Constraints
(
IEEE Computer Society
, 2022 , Article)
Distributed learning algorithms aim to leverage distributed and diverse data stored at users' devices to learn a global phenomena by performing training amongst participating devices and periodically aggregating their local ...
A Weighted Machine Learning-Based Attacks Classification to Alleviating Class Imbalance
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Institute of Electrical and Electronics Engineers Inc.
, 2021 , Article)
The Industrial Internet of Things (IIoT) has become very popular in recent years. However, IIoT is still an attractive and vulnerable target for attackers to exploit and experiment with different types of attacks. To ...
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
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Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. IoT is one of the fastest developing fields in the history of computing, with an ...