Manuscript Submission Deadline
Call for Papers
It has been predicted from the recent study that by the year 2020, around 50 billion devices are likely to use the wireless network services, which may lead to an exponential increase in data traffic resulting an extra burden on the existing network infrastructure to maintain QoS and QoE. To maintain QoS and QoE for the end users, the upcoming 5G networks backbone architecture can provide a huge amount of data rates by using bandwidth spectrum of heterogenous networks (HetNets) and such an environment is called as 5G HetNet. But, security and privacy are the main concern in 5G HetNets keeping in view of the various attacks launched by different attackers in this environment. It may result a performance degradation at various levels in this environment. The existing security solutions based on non-cryptographic and cryptographic methods may not be applicable directly to conquer these issues due to extra computation and communication costs. So, this Special Issue (SI) solicits the Deep Learning based security and privacy preservation solutions for handling the aforementioned challenges in 5G HetNet. It has been found in the literature that deep learning based models and techniques are widely used in wide variety of problems in 5G HetNets. Some of the major topics for the special issue include, but are not limited to:
- Security, integrity and privacy solutions for 5G HetNets.
- Energy-aware solutions for security of 5G HetNets.
- Deep Learning based message authentication in 5G HetNets.
- Deep Learning based physical layer security techniques for 5G HetNets.
- Error Correction coding with Deep Learning for 5G HetNets security.
- Security of SDN based 5G HetNets.
- Intrusion detection/ prevention techniques in 5G HetNets.
- Authentication and authorization for 5G HetNets security.
- Low power based deep learning techniques for security of 5G HetNets.
- Physical Layer Security of 5G HetNets.
- New cryptographic algorithms for the security and privacy of 5G HetNets.
- Data security using Deep Learning in 5G HetNets.
- Deep Learning based security protocols for secure communication in 5G HetNets.
Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines.
All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select “March 2021/Deep Learning Models and Techniques for Security and Privacy Preservation in 5G HetNets” from the drop-down menu of Topic/Series titles.
Manuscript Submission Deadline: 30 April 2020
Initial Decision: 30 June 2020
Revised Manuscript Due: 30 August 2020
Final Decision: 30 September 2020
Final Manuscript Due: 30 October 2020
Publication Date: March 2021
Thapar Institute of Engineering and Technology, Patiala, India
Octavia A Dobre
Memorial University, Canada
Hong Kong Polytechnic University, Hong Kong
Embry-Riddle Aeronautical University, USA
Qatar University, Qatar