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Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases
2021
Journal of Healthcare Engineering
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This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. ...
The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. ...
of Education and King Abdulaziz University, Jeddah, Saudi Arabia. ...
doi:10.1155/2021/3277988
pmid:34150188
pmcid:PMC8197673
fatcat:6lqbbsggknbfnk3aa4f3w2cfnq
SDN-Enabled IoT Anomaly Detection Using Ensemble Learning
[chapter]
2020
IFIP Advances in Information and Communication Technology
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We use a deep auto-encoder to extract handy features for stacking into an ensemble learning model. ...
Aggressive behavior change, due to increased attacker's sophistication, and the heterogeneity of the data in IoT have proven that securing IoT devices is a making challenge. ...
Part of this study was funded by the ICS-CoE Core Human Resources Development Program. Additional support was provided by the JST CREST Grant Number JPMJCR1783, Japan. ...
doi:10.1007/978-3-030-49186-4_23
fatcat:ppxqo7d33na2bkjimsgc3jeap4
Cyberattack and Fraud Detection Using Ensemble Stacking
2022
AI
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Smart devices are used in the era of the Internet of Things (IoT) to provide efficient and reliable access to services. ...
Cyberattacks have been detected using various techniques, such as deep learning and machine learning. ...
Data Availability Statement: Available in References
Conflicts of Interest: The authors declare no conflict of interest. AI 2022, 3 ...
doi:10.3390/ai3010002
fatcat:fsjwifa5nrbvhd2elrpium7owu
HealthFog: An ensemble deep learning based Smart Healthcare System for Automatic Diagnosis of Heart Diseases in integrated IoT and fog computing environments
2019
Future generations computer systems
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2019 pre-print arXiv:1911.06633v1 fatcat:b7rw6zamfvfftm7c3hpmz4565u
We proposed a novel framework called HealthFog for integrating ensemble deep learning in Edge computing devices and deployed it for a real-life application of automatic Heart Disease analysis. ...
The major bottleneck being faced currently in these cloud frameworks is their limited scalability and hence inability to cater to the requirements of centralized Internet of Things (IoT) based compute ...
We would also like to thank Samodha Pallewatta, Shashikant Ilager (CLOUDS Lab, University of Melbourne) and Shikhar Tuli (Indian Institute of Technology, Delhi) for their valuable comments on improving ...
doi:10.1016/j.future.2019.10.043
fatcat:eqhosiszbvafzhy7wjkr3poiwe
Learning-Based Methods for Cyber Attacks Detection in IoT Systems: Methods, Analysis, and Future Prospects
2022
Electronics
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For learning-based methods, both machine and deep learning methods are presented and analyzed in relation to the detection of cyber attacks in IoT systems. ...
This article primarily focuses on the IoT system/framework, the IoT, learning-based methods, and the difficulties faced by the IoT devices or systems after the occurrence of an attack. ...
Fuzzers, analysis, backdoor, [81] ADS-based deep learning DoS, generic, exploits, reconnaissance, shellcode, F-SVM, DMM, CVT, DBN, RNN, TANN, DNN, and ensemble-DNN NSL-KDD and UNSW-NB15 and worms DoS, ...
doi:10.3390/electronics11091502
fatcat:stweql4ru5behg2scpumznzy6i
Detecting IoT Attacks Using an Ensemble Machine Learning Model
2022
Future Internet
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Using the proposed method, based on historical data, an ensemble machine learning model is built in the cloud, followed by the real-time detection of attacks on fog nodes. ...
By incorporating fog computing into IoT, attacks can be detected in a short amount of time, as the distance between IoT devices and fog devices is smaller than the distance between IoT devices and the ...
Acknowledgments: This work was carried out with the support of the EU H2020 NGIAtlantic project under agreement No. OC3-292.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/fi14040102
fatcat:icnld25ju5gnjceoyqsrlzlbwa
Cyberattacks Detection in IoT-Based Smart City Applications Using Machine Learning Techniques
2020
International Journal of Environmental Research and Public Health
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In recent years, the widespread deployment of the Internet of Things (IoT) applications has contributed to the development of smart cities. ...
In this paper, we explore an attack and anomaly detection technique based on machine learning algorithms (LR, SVM, DT, RF, ANN and KNN) to defend against and mitigate IoT cybersecurity threats in a smart ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/ijerph17249347
pmid:33327468
pmcid:PMC7764956
fatcat:syieikibrvfexjbpg7u5nzf5hm
Threat Detection using Machine/Deep Learning in IOT Environments
2020
International journal of computer networks and communications security
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This paper aims to improve the security in IOT environments. In any of the IOT networks the unknown and knows flaws can be a backdoor for any adversary. ...
The quality of human life is improving day by day and IOT plays a very important role in this improvement. Everything related to internet have some security concerns. ...
In [9] the author used a dataset named NSL-KDD, which is an application of deep learning in the area of computer security. ...
doi:10.47277/ijcncs/8(8)2
fatcat:hxj6oo2mqjhnnl4vqsnaciv2we
Machine Learning Methods for Detecting Internet-of-Things (IoT) Malware
2021
International Journal of Cognitive Informatics and Natural Intelligence
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Therefore, the results of this study can help both the researchers and application developers in designing and building intelligent malware detection systems for IoT devices. ...
This study aims to analyze the performance of machine learning models for detecting Internet of Things malware utilizing a recent IoT dataset. ...
As different IoT datasets may have various features and attack types, experimentation with multiple datasets with a deep learning approach to feature selection will be insightful. ...
doi:10.4018/ijcini.286768
fatcat:okfhclio5jefnjdbvjj7cbjs2e
Secure IIoT-Enabled Industry 4.0
2021
Sustainability
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Subsequently, we propose a deep learning (DL)-enabled novel hybrid architecture that can efficiently and timely tackle distributed, multivariant, lethal botnet attacks in industrial IoT. ...
Our devised mechanism shows promising results in terms of high detection accuracy with a trivial trade-off in speed efficiency, assuring the proposed scheme as an optimal and legitimate cyber defense in ...
Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/su132212384
fatcat:5qfwtksfybgdll74l5gslfd724
COVED: A Hardware Accelerated Soft Computing Enabled Intelligent Value Chain Based Diagnostic Automation for nCOVID-19 Estimation and Identification
2021
International Journal of Statistics in Medical Research
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the use of ensemble deep transfer learning. ...
Results: Over the HRCT image dataset, the developed deep ensemble model is collated to different state-of-the-art transfer learning (TL) models. ...
ACKNOWLEDGEMENTS The authors also acknowledge The Neotia University and Adamas University for providing excellent research infrastructure and for necessary funding. ...
doi:10.6000/1929-6029.2021.10.14
fatcat:42ewzz2v5zekzgu3nwrog3d6mu
Machine-Learning-Based Darknet Traffic Detection System for IoT Applications
2022
Electronics
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Thus, in this paper, we develop, investigate, and evaluate the performance of machine-learning-based Darknet traffic detection systems (DTDS) in IoT networks. ...
Our empirical performance analysis demonstrates that bagging ensemble techniques (BAG-DT) offer better accuracy and lower error rates than other implemented supervised learning techniques, scoring a 99.50% ...
Section 4 presents and discusses our experimental results of the proposed ML-DTDS-IoT. ...
doi:10.3390/electronics11040556
fatcat:ltk2mqppizbndcufgxd3htx44y
Guest Editorial: Special Section on Advanced Deep Learning Algorithms for Industrial Internet of Things
2021
IEEE Transactions on Industrial Informatics
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Wei is a Senior Member of CCF. ...
His current research interests include Internet of Things, wireless sensor networks, image processing, mobile computing, distributed computing, pervasive computing, and sensor data clouds. Dr. ...
Incorporating advanced deep learning algorithms into IIoT can provide radical innovations in data analysis and pathbreaking industry applications. ...
doi:10.1109/tii.2020.3026551
fatcat:drpgvjt6gnafliu3ch7zxebn7y
Artificial intelligence in deep learning algorithms for multimedia analysis
2020
Multimedia tools and applications
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Applications. ...
Acknowledgements We would like to express our appreciation to all the authors for their informative contributions and the reviewers for their support and constructive critiques in making this special issue ...
understanding, and audio/speech recognition) -AI in deep multimodal learning -AI in deep learning for data indexing and retrieval -Multi-view and cross-view deep learning based visual content analysis ...
doi:10.1007/s11042-020-09232-7
fatcat:cdbw6rsv4vhw5ggvy6lsidyh5q
Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT
2022
IoT
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and deep learning techniques. ...
Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/iot3020017
fatcat:b35kzrauxnd5bdh2qlispr4gcu
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