scholarly journals A Review Of The Issues And Challenges In IoT Security Using Machine Learning Techniques

2021 ◽  
Vol 9 (4) ◽  
pp. 499-502
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Security Threats ◽  
X Rays ◽  
Iot Security ◽  
Learning Techniques ◽  
Human Endeavor ◽  
Security Challenges ◽  
Iot Devices ◽  
The Internet Of Things

In a typical IoT network, a sensor connects to a controller using a wireless connection. Controllers collect data from sensors and sends the data for storage and analysis[1]. These controllers work with actuators that translate an electrical input to a physical action. The internet of things (IoT), have found application in different areas of human endeavor including healthcare, government, supply chain, cities, manufacturing, etc. and it is estimated that the number of connected devices will reach 50 billion by 2020[2] With the increasing number of devices comes an increase in the the varying number of security threats to the IoT network [3]. To contain these threats, a secure-by-design approach should be adopted as this will help the IoT devices to anticipate and neutralize the ever changing nature of the threats as against older systems where security was handled as it presents itself [2] This paper x-rays the security challenges in IoT networks and the application of machine learning (Supervised learning, Unsupervised learning and Reinforcement learning) in tackling the security challenges

scholarly journals Securing IoT Environment using Machine Learning Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 870-873
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Learning Approach ◽  
Learning Approaches ◽  
Digital Devices ◽  
Learning Techniques ◽  
Security Challenges ◽  
Machine Learning Approach ◽  
The World ◽  
Iot Devices

One of the most dynamic and invigorate advancement in information technology is advent of Internet of Things (IoT). IoT is territory of interrelated computational and digital devices with intelligence to transfer data. Along with swift expansion of IoT devices through the world security of things is not at expected height. As a consequence of ubiquitous nature of IoT environment most of the user do not have expertise or willingness to secure devices by themselves. Machine learning approach could be very effective to address security challenges in IoT environment. In recent related papers, the researcher have used machine learning techniques, approaches or methods for securing things in IoT environment. This paper attempts to review the related research on machine learning approaches to secure IoT devices

scholarly journals Monitoring Real Time Security Attacks for IoT Systems Using DevSecOps: A Systematic Literature Review

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 154
Author(s):  
Ahmed Bahaa ◽  
Ahmed Abdelaziz ◽  
Abdalla Sayed ◽  
Laila Elfangary ◽  
Hanan Fahmy
Keyword(s):  
Machine Learning ◽  
Denial Of Service ◽  
Attack Detection ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Precise Analysis ◽  
Development Processes ◽  
Iot Devices ◽  
Public Datasets ◽  
The Internet Of Things

In many enterprises and the private sector, the Internet of Things (IoT) has spread globally. The growing number of different devices connected to the IoT and their various protocols have contributed to the increasing number of attacks, such as denial-of-service (DoS) and remote-to-local (R2L) ones. There are several approaches and techniques that can be used to construct attack detection models, such as machine learning, data mining, and statistical analysis. Nowadays, this technique is commonly used because it can provide precise analysis and results. Therefore, we decided to study the previous literature on the detection of IoT attacks and machine learning in order to understand the process of creating detection models. We also evaluated various datasets used for the models, IoT attack types, independent variables used for the models, evaluation metrics for assessment of models, and monitoring infrastructure using DevSecOps pipelines. We found 49 primary studies, and the detection models were developed using seven different types of machine learning techniques. Most primary studies used IoT device testbed datasets, and others used public datasets such as NSL-KDD and UNSW-NB15. When it comes to measuring the efficiency of models, both numerical and graphical measures are commonly used. Most IoT attacks occur at the network layer according to the literature. If the detection models applied DevSecOps pipelines in development processes for IoT devices, they were more secure. From the results of this paper, we found that machine learning techniques can detect IoT attacks, but there are a few issues in the design of detection models. We also recommend the continued use of hybrid frameworks for the improved detection of IoT attacks, advanced monitoring infrastructure configurations using methods based on software pipelines, and the use of machine learning techniques for advanced supervision and monitoring.

scholarly journals Machine Learning Applications on Agricultural Datasets for Smart Farm Enhancement

Machines ◽  
2018 ◽  
Vol 6 (3) ◽  
pp. 38 ◽  
Author(s):  
Fabrizio Balducci ◽  
Donato Impedovo ◽  
Giuseppe Pirlo
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Agricultural Field ◽  
Heterogeneous Information ◽  
Learning Techniques ◽  
Farm Model ◽  
Machine Learning Applications ◽  
Crop Harvest ◽  
The Right ◽  
The Internet Of Things

This work aims to show how to manage heterogeneous information and data coming from real datasets that collect physical, biological, and sensory values. As productive companies—public or private, large or small—need increasing profitability with costs reduction, discovering appropriate ways to exploit data that are continuously recorded and made available can be the right choice to achieve these goals. The agricultural field is only apparently refractory to the digital technology and the “smart farm” model is increasingly widespread by exploiting the Internet of Things (IoT) paradigm applied to environmental and historical information through time-series. The focus of this study is the design and deployment of practical tasks, ranging from crop harvest forecasting to missing or wrong sensors data reconstruction, exploiting and comparing various machine learning techniques to suggest toward which direction to employ efforts and investments. The results show how there are ample margins for innovation while supporting requests and needs coming from companies that wish to employ a sustainable and optimized agriculture industrial business, investing not only in technology, but also in the knowledge and in skilled workforce required to take the best out of it.

Data Analytics and Modeling in IoT-Fog Environment for Resource Constrained IoT-Applications - A Review

2021 ◽  
Vol 14 ◽  
Author(s):  
Omar Farooq ◽  
Parminder Singh
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Data Classification ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Resource Constrained ◽  
Learning Techniques ◽  
Iot Devices ◽  
The Right ◽  
Continuous Use

Introduction: The emergence of the concepts like Big Data, Data Science, Machine Learning (ML), and the Internet of Things (IoT) has added the potential of research in today's world. The continuous use of IoT devices, sensors, etc. that collect data continuously puts tremendous pressure on the existing IoT network. Materials and Methods: This resource-constrained IoT environment is flooded with data acquired from millions of IoT nodes deployed at the device level. The limited resources of the IoT Network have driven the researchers towards data Management. This paper focuses on data classification at the device level, edge/fog level, and cloud level using machine learning techniques. Results: The data coming from different devices is vast and is of variety. Therefore, it becomes essential to choose the right approach for classification and analysis. It will help optimize the data at the device edge/fog level to better the network's performance in the future. Conclusion: This paper presents data classification, machine learning approaches, and a proposed mathematical model for the IoT environment.

Coronavirus Detection and Classification Using X-Rays and CT Scans with Machine Learning Techniques

2021 ◽  
pp. 277-286
Author(s):  
Moulana Mohammed ◽  
P. V. V. S. Srinivas ◽  
Veldi Pream Sai Gowtham ◽  
Adapa V. Krishna Raghavendra ◽  
Garapati Khyathi Lahari
Keyword(s):  
Machine Learning ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
Learning Techniques

scholarly journals A Comparative Systematic Literature Review on Knee Bone Reports from MRI, X-Rays and CT Scans Using Deep Learning and Machine Learning Methodologies

Diagnostics ◽  
2020 ◽  
Vol 10 (8) ◽  
pp. 518 ◽  
Author(s):  
Hafsa Khalid ◽  
Muzammil Hussain ◽  
Mohammed A. Al Ghamdi ◽  
Tayyaba Khalid ◽  
Khadija Khalid ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Literature Review ◽  
Ct Scan ◽  
Systematic Literature Review ◽  
Machine Learning Techniques ◽  
Ct Scans ◽  
X Rays ◽  
X Ray ◽  
Learning Techniques

The purpose of this research was to provide a “systematic literature review” of knee bone reports that are obtained by MRI, CT scans, and X-rays by using deep learning and machine learning techniques by comparing different approaches—to perform a comprehensive study on the deep learning and machine learning methodologies to diagnose knee bone diseases by detecting symptoms from X-ray, CT scan, and MRI images. This study will help those researchers who want to conduct research in the knee bone field. A comparative systematic literature review was conducted for the accomplishment of our work. A total of 32 papers were reviewed in this research. Six papers consist of X-rays of knee bone with deep learning methodologies, five papers cover the MRI of knee bone using deep learning approaches, and another five papers cover CT scans of knee bone with deep learning techniques. Another 16 papers cover the machine learning techniques for evaluating CT scans, X-rays, and MRIs of knee bone. This research compares the deep learning methodologies for CT scan, MRI, and X-ray reports on knee bone, comparing the accuracy of each technique, which can be used for future development. In the future, this research will be enhanced by comparing X-ray, CT-scan, and MRI reports of knee bone with information retrieval and big data techniques. The results show that deep learning techniques are best for X-ray, MRI, and CT scan images of the knee bone to diagnose diseases.

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