Big Data Analytics in Fog-Enabled IoT Networks
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Big Data Analytics in Fog-Enabled IoT Networks
Towards a Privacy and Security Perspective
Gupta, Brij B.; Tripathi, Rakesh; Chui, Kwok Tai; Gupta, Govind P.
Taylor & Francis Ltd
10/2024
216
Mole
9781032206455
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1. Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices. 2. IoMT based Smart Health Monitoring: The Future of HealthCare. 3. A Review on Intrusion Detection Systems and Cyber Threat Intelligence for Secure IoT-Enabled Network: Challenges and Directions. 4. Self-Adaptive Application Monitoring for Decentralized Edge Frameworks. 5. Federated Learning and Its Application in Malware Detection. 6. An Ensemble XGBoost Approach for the Detection of Cyber-Attacks in the Industrial IOT Domain. 7. A Review on IoT for the Application of Energy, Environment, and Waste Management: System Architecture and Future Directions. 8. Analysis of Feature Selection Methods for Android Malware Detection Using Machine Learning Techniques. 9. An Efficient Optimizing Energy Consumption Using Modified Bee Colony Optimization in Fog and IoT Networks.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
big data analytics;Fog-enabled IoT Networks;fog computing;smart grid applications;Cyber threat detection;blockchain;IoT Device;IoT Network;F1 Score;Random Forest;Malware Detection;IoT Application;IoT Big Data;Latent Dirichlet Allocation;IoT System;CTI;Make Span;Dl Technique;AUC Roc;Standard PSO;Honey Bee;Gradient Boosting;Edge Computing;VM;Cloud Data Centres;GBDT;Binary Dataset;Feature Selection;Dl Model;Edge Nodes
1. Deep Learning Techniques in Big Data-Enabled Internet-of-Things Devices. 2. IoMT based Smart Health Monitoring: The Future of HealthCare. 3. A Review on Intrusion Detection Systems and Cyber Threat Intelligence for Secure IoT-Enabled Network: Challenges and Directions. 4. Self-Adaptive Application Monitoring for Decentralized Edge Frameworks. 5. Federated Learning and Its Application in Malware Detection. 6. An Ensemble XGBoost Approach for the Detection of Cyber-Attacks in the Industrial IOT Domain. 7. A Review on IoT for the Application of Energy, Environment, and Waste Management: System Architecture and Future Directions. 8. Analysis of Feature Selection Methods for Android Malware Detection Using Machine Learning Techniques. 9. An Efficient Optimizing Energy Consumption Using Modified Bee Colony Optimization in Fog and IoT Networks.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
big data analytics;Fog-enabled IoT Networks;fog computing;smart grid applications;Cyber threat detection;blockchain;IoT Device;IoT Network;F1 Score;Random Forest;Malware Detection;IoT Application;IoT Big Data;Latent Dirichlet Allocation;IoT System;CTI;Make Span;Dl Technique;AUC Roc;Standard PSO;Honey Bee;Gradient Boosting;Edge Computing;VM;Cloud Data Centres;GBDT;Binary Dataset;Feature Selection;Dl Model;Edge Nodes
