Call for Papers: Special Issue on "Emerging AI-supported Wireless Defense Systems for Edge Computing Networks"

Guest Editors:

  • Dr. Hichem Sedjelmaci,  Ericsson R&D, France, Email: hichem.sedjelmaci@ericsson.com 
  • Dr. Moayad Aloqaily, xAnalytics, Ottawa, ON, Canada. Email: maloqaily@xanalytics.c
  • Prof. Jaime Lloret, Universitat Politecnica de Valencia, Spain, Email: jlloret@dcom.upv.e
  • Prof. Sidi-Mohammed Senouci,University of Burgundy, France, Email: Sidi-Mohammed.Senouci@ubourgogne.fr
  • Prof. Soumaya Cherkaoui,University of Sherbrooke, Canada, Email: Soumaya.Cherkaoui@usherbrooke.ca
  • Prof. Jiajia Liu,Northwestern PolytechnicalUniversity, China, Email: liujiajia@nwpu.edu.cn

 Given their capabilities to support a variety of services and applications, Fifth Generation (5G) and Beyond (B5G) cellular networks have revolutionized computer communication and networking. Some of the applications which have benefited from 5G are: self-driving vehicles, drones, augmented reality, home and health monitoring, factory automation and content caching. Edge computing, defined also as a Mobile Edge Computing (MEC) are adapted in 5G networks to overcome issues of computation overhead and considerably reduce network latency. This is achieved by bringing cloud capabilities near end-devices, such that the massive amount of data and intensive computed tasks are analyzed and processed at powerful edge servers.


Due to the sensitivity of data that is handled at MEC servers, the risk of massive cyber-attacks against wireless-based edge computing networks is highly likely. Recently, cyber security experts have identified a couple of cyber and network vulnerabilities at radio access networks and edge computing levels. This is mainly due to the heterogeneity of communication technologies and the virtualization (softwarization) of network functions. Furthermore, security attacks are increasing and are becoming more sophisticated. On the contrary, traditional defense systems (e.g., firewall, intrusion detection and prediction systems based on rule detection techniques) do not have the capability to detect and identify smart and complex threats, defined also as Zero-Day threats. Therefore, a new era of wireless defense systems that rely on Artificial Intelligence (AI) should be deployed to detect and identify the unknown misbehaviors of attackers (e.g. zero-day attacks).


A plethora of AI-supported solutions are being developed at research and development laboratories to protect wireless and wired networks from known and unknown mis-behaviors executed by network attacks. Machine learning technologies (supervised, non-supervised and reinforcement learning algorithms) are used to enhance attack detection mechanisms and reduce false positives. Similarly, different contemporary AI techniques in addition to blockchain technology can be applied to determine whether edge computing servers are prone to attacks, whether at the storage or communication levels. Such mechanisms will not only support security and privacy at the edge, but also will detect any presumptive attacks at the cloud.


This special issue invites original research that investigates emerging AI-supported defense systems in wireless-based MEC networks. 

Potential topics include but are not limited to the following:

  • Wireless network architectures to prevent AI-attacks against MEC devices.
  • Wireless network attack detection and prevention solutions supported by advanced AI algorithms.
  • Cyber threat intelligence: using machine learning algorithms to secure the wireless edge.
  • Wireless defense games to protect MEC networks.
  • Distributed Intrusion Detection System (IDS) and Intrusion Prevention System (IPS) supported by AI algorithms.
  • Adapting Blockchain for Privacy-Critical and Data-Sensitive Applications at the edge.
  • Applying AI algorithms to support Blockchain technology in critical infrastructures.
  • Using blockchain for transaction authentication at the edge.
  • Automated attacks mitigation based on AI in wireless edge computing networks.
  • Federated edge learning-based data analytics to detect threats. 

Submission Deadline (Tentative):

  • Submission deadline: 30 December, 2021 
  • First round notification: 28 February, 2022
  • Second round due:  30 April, 2022  
  • Final notification: 30 June, 2022