MACHINE LEARNING TECHNIQUES FOR SECURE AND ENERGY- 2023 - 2023 – Special Issue on : “Machine learning techniques for secure and energy-efficient IoT networks”

Website https://airccse.org/journal/sicfp23-1.html | Edit Freely

Category IoT;Networks;Routing protocols;Transmission protocols;wireless sensor networks

Deadline: August 30, 2023 | Date: October 15, 2023-October 16, 2023

Venue/Country: Chennai, India, India

Updated: 2023-04-27 19:48:44 (GMT+9)

Call For Papers - CFP

https://airccse.org/journal/sicfp23-1.html

Guest Editors:

Arvind Dhaka

Manipal University Jaipur, India

Siddhartha Chauhan

NIT Hamirpur, India

Edmar Candeia Gurjao

Federal University of Campina Grande, Brazil

Amita Nandal

Manipal University Jaipur, India

Theme and Scope

The Internet of Things (IoT) is a new paradigm for the development of ubiquitous computing, which enables the connection of billions of heterogeneous devices with each other and the cloud. IoT is being used in many different applications, from smart homes to big data analytics. However, the IoT infrastructure is vulnerable to security threats, such as data breaches, man-in-the-middle attacks, and malicious actors. Furthermore, energy consumption is an important factor in IoT networks, as many IoT devices are battery-powered and need to conserve energy. Machine learning techniques are being increasingly used to enhance the security and energy efficiency of the IoT network. Machine learning algorithms can be used to detect malicious activities in the network and protect against them. It can also be used to identify anomalies in the network, which can be used to detect potential security threats. Furthermore, machine learning techniques can be used to optimize energy consumption in the network, by predicting energy demands and adjusting the network accordingly.

The Internet of Things (IoT) is a network of physical objects that are embedded with sensors, software, and other technologies for the purpose of collecting and exchanging data. As the use of IoT networks increase, security and energy efficiency become increasingly important topics of research. Machine learning techniques can be used to improve the security and energy efficiency of IoT networks. This call for papers invites authors to submit original research on the use of machine learning techniques for secure and energy-efficient IoT networks. We welcome both theoretical and applied research papers on topics such as, but not limited to:

Design of secure and energy-efficient IoT networks

Machine learning techniques for secure and energy-efficient IoT networks

Security and privacy issues in IoT networks

Energy-efficient routing protocols in IoT networks

Secure and energy-efficient data transmission protocols

Secure and energy-efficient access control

Security and energy-efficiency in wireless sensor networks

Machine learning techniques for anomaly detection in IoT networks

Machine learning techniques for intrusion detection in IoT networks

AI-based techniques for secure and energy-efficient IoT networks

Blockchain-based techniques for secure and energy-efficient IoT networks

Notes for Prospective Authors

Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Issue. Manuscripts should be written in English and strictly follow the guideline of the Journal IJCNC. The manuscripts should be submitted to one of the guest editors through email iot2023aircconline.com.

Tentative timeline for this special issue:

Submission Deadline : 30 August 2023

Notification of First Round Decision : 15 October 2023

Revised Paper Submission Deadline : 15 November 2023

Notification of Final Decision : 15 December 2023

For more details please visit : http://airccse.org/journal/ijcnc.html