Year –
Type
Research area
sub-areas
sub-areas
sub-areas
sub-areas
sub-areas
sub-areas
sub-areas
Staff member
Clear all

3 publications found

Towards a quantum-resilient future: Strategies for transitioning to post-quantum cryptography

Abdullah Aydeger, Engin Zeydan, Awaneesh Kumar Yadav, Kasun T. Hemachandra, Madhusanka Liyanage

Conference NoF 2024 15th International Conference on Network of the Future (NoF) , pp. 195-203 · 2024 · Cited by 116

Communication Theory and CodingMobile and Next-Generation Networks

Abstract

The imminent emergence of quantum computers poses unprecedented challenges to the current cryptographic standards that secure vital digital communications and data worldwide. This paper addresses the vulnerabilities of existing cryptographic systems susceptible to quantum-based attacks and emphasizes a dedicated transition plan to post-quantum cryptography (PQC). We first examine the current state of cryptographic practices, highlight the quantum threat, and present the framework and need for PQC. In addition, a comprehensive strategy is presented that encompasses the identification, protection, detection, response, and recovery phases and integrates PQC into existing security infrastructures, specifically the Cybersecurity Framework Version 2.0 of the National Institute of Standards and Technology (NIST). Methods and technologies discussed include the development of robust PQC algorithms, tools …

Federated learning for 6G networks: Navigating privacy benefits and challenges

Chamara Sandeepa, Engin Zeydan, Tharaka Samarasinghe, Madhusanka Liyanage

Journal IEEE Open Journal of the Communications Society , vol. 6 , pp. 90-129 · 2024 · Cited by 44

Machine Learning and Pattern RecognitionMobile and Next-Generation NetworksNetwork Optimization and Intelligence

Abstract

The upcoming Sixth Generation (6G) networks aim for fully automated, intelligent network functionalities and services. Therefore, Machine Learning (ML) is essential for these networks. Given stringent privacy regulations, future network architectures should use privacy-preserved ML for their applications and services. Federated Learning (FL) is expected to play an important role as a popular approach for distributed ML, as it protects privacy by design. However, many practical challenges exist before FL can be fully utilized as a key technology for these future networks. We consider the vision of a 6G layered architecture to evaluate the applicability of FL-based distributed intelligence. In this paper, we highlight the benefits of using FL for 6G and the main challenges and issues involved. We also discuss the existing solutions and the possible future directions that should be taken toward more robust and trustworthy FL …

Multiple Regression Analytical Approach to Valuing the Radio Spectrum Bands for 5G Networks

Surath Jayasinghe, Abhaya Sumanasena, Kasun T. Hemachandra

Conference IATMSI 2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) , vol. 2 , pp. 1-6 · 2024 · Cited by 0

Wireless CommunicationsMobile and Next-Generation Networks

Abstract

The radio spectrum is a scarce and vital resource. Every country in the world has sovereign control over its spectrum. Demand for the spectrum in terms of mobile communication-related applications is increasing continuously. As a result, Mobile network operators (MNO) are keen to acquire additional spectrum bands to deploy in mobile networks. 5G technology, as the latest generation of mobile telephony with many advanced features has opened many doors to other industries such as virtual reality, the Internet of Things(IoT), smart cities, smart health care, etc. and demand for them is trending. As a result of its technological advancements and the economic benefits of 5th generation technology (5G), network operators are keen to migrate to 5G. Due to the excessive demand for the spectrum, the true value of the spectrum has gone up drastically. Therefore, understanding the true value of spectrum has become a …