Call for Papers: The New Era of Computer Network by using Machine Learning


The network has come a long way with the advances of Software Defined Network (SDN) and Network Function Virtualization (NFV). It generates a huge amount of data (big data) in daily life. Therefore, it is very difficult to manually analyze all the data by the network professionals and to decide whether the network is good enough to manage all the data or an adjustment is required. Moreover, security issues are continuously increasing in the network due to the presence of numerous hackers and malicious users. Therefore, there should be a strong security technique to protect data against hackers and malicious users. Currently, Machine Learning (ML) algorithms are used for network management by many researchers.

Machine learning is the study of mathematical modelā€based algorithms that improve automatically through experience. ML algorithms are based on data to make decisions without being explicitly programmed to do so. There are many applications of ML in daily life, such as smart email categorization, chatbot, marketing, healthcare, gaming, plagiarism check, autonomous vehicle, and many more. Nowadays, ML is used in industry and academia due to the data-driven feature for achieving the high performance of a network. ML algorithms can be run throughout the network without the need for any external hardware to predict the potential problems of the network before occurring. In addition, ML algorithms can also recognize the problems of a network and can make a recommendation to fix them. Thus, the network professionals can take a decision to manage the network efficiently. Nowadays, new attacks are being developed every day by attackers, and it is very difficult to detect them by using traditional intrusion detection techniques. ML algorithms can be developed to train a network for detecting sophisticated attacks, which are similar to the already defined known attacks. It is important to improve the algorithms, so that there is an efficient trade-off between learning cost and detection accuracy. Recent research has also shown the negative impact of ML as these advanced fields support new attack tools by using the adversarial ML techniques to develop new attacks. Attackers and malicious users can also hack ML algorithms by altering the training data and modifying the classification function of ML, which can directly affect the detection accuracy of a network. These types of threats are very critical. Therefore, novel techniques of cybersecurity must be developed to protect a network.

This special issue gives a platform for researchers, academicians, and industry professionals to present their research work on ML in network management. This special issue aims to address the challenges and issues of applying ML in networks.


Topics of interest include, but are not limited to, the following scope:

  • Learning from network data  
  • Issues to apply ML in networks
  • ML for network management
  • ML for service placement
  • ML for predictive analysis in networks
  • ML for network  slicing optimization
  • Pattern recognition and classification for networks
  • Analysis, modelling, and visualization of networks using ML
  • ML for 5G
  • Challenges of black-box attacks in ML methods
  • ML in network security and privacy 
  • Security threats, intrusions, and malware detection exploiting ML methods
  • ML-driven attack model generation and specification ML-based cryptographic protocols in networks
  • ML-based identity management in networks
  • ML for big data security/cloud security/IoT security
  • Emerging technologies in network management

Important Dates

Manuscript submission deadline: 31 July 2022  Notification of acceptance: 31 October 2022 Submission of final revised paper: 30 November 2022 Publication of special issue (tentative): 31 December 2022

Submission Procedure

Authors should follow the MONET Journal manuscript format described at the journal site. Manuscripts should be submitted on-line through A copy of the manuscript should also be emailed to the Guest Editor at the following email address(es):

Guest Editors:

Suyel Namasudra, PhD (Lead Guest Editor)
National Institute of Technology Patna, Bihar, India

Prof. Pascal Lorenz
University of Haute-Alsace, Colmar, France

Uttam Ghosh, PhD
Meharry School of Applied Computer Sciences, Tennessee, USA