scholarly journals A machine learning approach for indirect human presence detection using IOT devices

2016 ◽  
Author(s):  
Rui Madeira ◽  
Luis Nunes
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Human Presence ◽  
Machine Learning Approach ◽  
Human Presence Detection ◽  
Iot Devices

scholarly journals Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
John Foley ◽  
Naghmeh Moradpoor ◽  
Henry Ochenyi
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Vulnerability Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Lossy Networks ◽  
Global Networks ◽  
Machine Learning Approach ◽  
Iot Devices ◽  
Network Metrics

One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.

Securing IoT Devices using Dynamic Power Management: Machine Learning Approach

2020 ◽  
pp. 1-1
Author(s):  
Nikhil Chawla ◽  
Arvind Singh ◽  
Harshit Kumar ◽  
Monodeep Kar ◽  
Saibal Mukhopadhyay
Keyword(s):  
Machine Learning ◽  
Power Management ◽  
Learning Approach ◽  
Dynamic Power Management ◽  
Dynamic Power ◽  
Machine Learning Approach ◽  
Iot Devices

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

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