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Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

Ahmed I. Iskanderani, Ibrahim M. Mehedi, Abdulah Jeza Aljohani, Mohammad Shorfuzzaman, Farzana Akther, Thangam Palaniswamy, Shaikh Abdul Latif, Abdul Latif, Aftab Alam, Dilbag Singh
2021 Journal of Healthcare Engineering  
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]

Enkhtur Tsogbaatar, Monowar H. Bhuyan, Yuzo Taenaka, Doudou Fall, Khishigjargal Gonchigsumlaa, Erik Elmroth, Youki Kadobayashi
2020 IFIP Advances in Information and Communication Technology  
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

Raha Soleymanzadeh, Mustafa Aljasim, Muhammad Waseem Qadeer, Rasha Kashef
2022 AI  
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

Shreshth Tuli, Nipam Basumatary, Sukhpal Singh Gill, Mohsen Kahani, Rajesh Chand Arya, Gurpreet Singh Wander, Rajkumar Buyya
2019 Future generations computer systems  
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

Usman Inayat, Muhammad Fahad Zia, Sajid Mahmood, Haris M. Khalid, Mohamed Benbouzid
2022 Electronics  
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

Vikas Tomer, Sachin Sharma
2022 Future Internet  
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

Md Mamunur Rashid, Joarder Kamruzzaman, Mohammad Mehedi Hassan, Tasadduq Imam, Steven Gordon
2020 International Journal of Environmental Research and Public Health  
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  
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  
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

Zeeshan Hussain, Adnan Akhunzada, Javed Iqbal, Iram Bibi, Abdullah Gani
2021 Sustainability  
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

Swarnava Biswas, Debajit Sen, Dinesh Bhatia, Moumita Mukherjee, Department of Physics, School of Basic and Applied Sciences, Adamas University, Kolkata, West Bengal, India
2021 International Journal of Statistics in Medical Research  
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

Qasem Abu Al-Haija, Moez Krichen, Wejdan Abu Elhaija
2022 Electronics  
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

Syed Hassan Ahmed, Victor Hugo Costa de Albuquerque, Wei Wei
2021 IEEE Transactions on Industrial Informatics  
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

Gwanggil Jeon, Marco Anisetti, Ernesto Damiani, Burak Kantarci
2020 Multimedia tools and applications  
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

Rubayyi Alghamdi, Martine Bellaiche
2022 IoT  
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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