Estimating Ore Production in Open-pit Mines Using Various Machine Learning Algorithms Based on a Truck-Haulage System and Support of Internet of Things

2020 ◽  
Author(s):  
Yosoon Choi ◽  
Hoang Nguyen ◽  
Xuan-Nam Bui ◽  
Trung Nguyen-Thoi ◽  
Sebeom Park
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Open Pit ◽  
Open Pit Mines ◽  
Haulage System

Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The 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.

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

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

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

2018 ◽  
Vol 22 (3) ◽  
pp. 199-200 ◽  
Author(s):  
Gunasekaran Manogaran ◽  
Naveen Chilamkurti ◽  
Ching-Hsien Hsu
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Special Issue

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

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.

Artificial Intelligence and Machine Learning Algorithms

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.

scholarly journals Multi-level host-based intrusion detection system for Internet of things

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Robin Gassais ◽  
Naser Ezzati-Jivan ◽  
Jose M. Fernandez ◽  
Daniel Aloise ◽  
Michel R. Dagenais
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Detection System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Network Activity ◽  
Machine Learning Techniques ◽  
Smart Devices ◽  
Automation System

AbstractThe growth of the Internet of things (IoT) has ushered in a new area of inter-connectivity and innovation in the home. Many devices, once separate, can now be interacted with remotely, improving efficiency and organization. This, however, comes at the cost of rising security vulnerabilities. Vendors are competing to create and release quickly innovative connected objects, without focusing on the security issues. As a consequence, attacks involving smart devices, or targeting them, are proliferating, creating threats to user’s privacy and even their physical security. Additionally, the heterogeneous technologies involved in IoT make attempts to develop protection on smart devices much harder. Most of the intrusion detection systems developed for those platforms are based on network activity. However, on many systems, intrusions cannot easily or reliably be detected from network traces. We propose a novel host-based automated framework for intrusion detection. Our work combines user space and kernel space information and machine learning techniques to detect various kinds of intrusions in smart devices. Our solution use tracing techniques to automatically get devices behavior, process this data into numeric arrays to train several machine learning algorithms, and raise alerts whenever an intrusion is found. We implemented several machine learning algorithms, including deep learning ones, to achieve high detection capabilities, while adding little overhead on the monitored devices. We tested our solution within a realistic home automation system with actual threats.

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