Machine learning algorithms towards merging of mobile edge computing and Internet of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

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Machine learning algorithms for smart data analysis in the Internet of things: an overview

Intelligent Wireless Communications ◽

10.1049/pbte094e_ch12 ◽

2021 ◽

pp. 307-327

Author(s):

Mohammed H. Alsharif ◽

Anabi Hilary Kelechi ◽

Imran Khan ◽

Mahmoud A. Albreem ◽

Abu Jahid ◽

...

Keyword(s):

Machine Learning ◽

Data Analysis ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

The Internet ◽

Smart Data ◽

The Internet Of Things

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Evaluation of Machine Learning Algorithms on Internet of Things (IoT) Malware Opcodes

10.1007/978-3-030-74753-4_12 ◽

2022 ◽

pp. 177-191

Author(s):

Adesola Anidu ◽

Zibekieni Obuzor

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms

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An Empirical Comparison of Supervised Machine Learning Algorithms for Internet of Things Data

2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA) ◽

10.1109/iccubea.2018.8697476 ◽

2018 ◽

Cited By ~ 2

Author(s):

Vijay Khadse ◽

Parikshit N. Mahalle ◽

Swapnil V Biraris

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Supervised Machine Learning ◽

Empirical Comparison

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Special issue on recent advancements in machine learning algorithms for internet of things

Design Automation for Embedded Systems ◽

10.1007/s10617-018-9213-4 ◽

2018 ◽

Vol 22 (3) ◽

pp. 199-200 ◽

Cited By ~ 1

Author(s):

Gunasekaran Manogaran ◽

Naveen Chilamkurti ◽

Ching-Hsien Hsu

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Special Issue

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A Framework of Novel Feature Set Extraction based Intrusion Detection System for Internet of Things using Hybrid Machine Learning Algorithms

2018 International Conference on Computing, Power and Communication Technologies (GUCON) ◽

10.1109/gucon.2018.8674952 ◽

2018 ◽

Cited By ~ 2

Author(s):

M. Nivaashini ◽

P. Thangaraj

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Hybrid Machine

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Survey of Machine Learning Algorithms to Detect Malware in Consumer Internet of Things Devices

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021500202 ◽

2021 ◽

Vol 30 (04) ◽

pp. 2150020

Author(s):

Luke Holbrook ◽

Miltiadis Alamaniotis

Keyword(s):

Neural Network ◽

Machine Learning ◽

Internet Of Things ◽

Deep Neural Network ◽

Learning Algorithms ◽

Cyber Attacks ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Support Vector ◽

Iot Devices

With the increase of cyber-attacks on millions of Internet of Things (IoT) devices, the poor network security measures on those devices are the main source of the problem. This article aims to study a number of these machine learning algorithms available for their effectiveness in detecting malware in consumer internet of things devices. In particular, the Support Vector Machines (SVM), Random Forest, and Deep Neural Network (DNN) algorithms are utilized for a benchmark with a set of test data and compared as tools in safeguarding the deployment for IoT security. Test results on a set of 4 IoT devices exhibited that all three tested algorithms presented here detect the network anomalies with high accuracy. However, the deep neural network provides the highest coefficient of determination R2, and hence, it is identified as the most precise among the tested algorithms concerning the security of IoT devices based on the data sets we have undertaken.

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Automation of a robotic cell using machine learning algorithms and internet of things

2017 IEEE Mexican Humanitarian Technology Conference (MHTC) ◽

10.1109/mhtc.2017.8006415 ◽

2017 ◽

Author(s):

E.H. Mayoral Arzaba ◽

O. Felix Beltran ◽

J. Cid Monjaraz ◽

F. Reyes, Cortes

Keyword(s):

Machine Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Robotic Cell

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Artificial Intelligence and Machine Learning Algorithms

Advances in Computer and Electrical Engineering - Challenges and Applications for Implementing Machine Learning in Computer Vision ◽

10.4018/978-1-7998-0182-5.ch008 ◽

2020 ◽

pp. 188-219

Author(s):

Amit Kumar Tyagi ◽

Poonam Chahal

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Computer Vision ◽

Big Data ◽

Deep Learning ◽

Internet Of Things ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Learning Techniques

With the recent development in technologies and integration of millions of internet of things devices, a lot of data is being generated every day (known as Big Data). This is required to improve the growth of several organizations or in applications like e-healthcare, etc. Also, we are entering into an era of smart world, where robotics is going to take place in most of the applications (to solve the world's problems). Implementing robotics in applications like medical, automobile, etc. is an aim/goal of computer vision. Computer vision (CV) is fulfilled by several components like artificial intelligence (AI), machine learning (ML), and deep learning (DL). Here, machine learning and deep learning techniques/algorithms are used to analyze Big Data. Today's various organizations like Google, Facebook, etc. are using ML techniques to search particular data or recommend any post. Hence, the requirement of a computer vision is fulfilled through these three terms: AI, ML, and DL.

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