Call For Workshop: Innovative Defense Technologies in 5G and Beyond Networks Using Machine Learning

Scope

The 5G and beyond networks have shown the potential to support billions of mobile devices, and are emerging as the central building block of future network. The booming of 5G and beyond mobile networks, however, comes with the risk of being more susceptible to security threats. Such threats also impose security challenges to networking technologies such as software-defined networking (SDN), network function virtualization (NFV), Internet of things (IoT) and mobile edge computing (MEC) in 5G and beyond mobile networks. Traditional security techniques might be insufficient as they may fail to meet requirements such as ultra-low latency and deterministic. In addition, they may not be available as cyberattacks evolved with the 5G and beyond would bring unprecedented security risks.
Nowadays, the Machine Learning (ML) technology is used in both industry and academia due to its data driven feature for achieving high performance of a network. ML algorithms can be applied throughout the network to predict potential problems without the need of any external resources. This makes ML a promising technique in detecting attacks in 5G and beyond networks, e.g. ones which are similar to the known attacks but are hard to detect by traditional algorithms. More importantly, ML in 5G and beyond security brings new possibilities in analyzing, modelling and detecting network threats. It also enables the learning of unprecedent attacks in emerging 5G and beyond networks so that they can be detected and addressed without the need for external resources.
This workshop provides a platform for researchers, academics and industry professionals to present their research work on ML in 5G and beyond network security, with the aim to address network threats by novel ML-based technology.

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Topics

Theoretical as well as experimental research works on the following topics (but are not limited to) are within the scope of this workshop:

  • ML in 5G and beyond security
  • ML driven attack model generation and specification
  • ML for big data security/cloud security/IoT security
  • ML based cryptographic protocols in network
  • ML based identity management in 5G and beyond network
  • ML for predicting user behavior in 5G and beyond network
  • Analysis, modelling and visualization of attacks using ML
  • Security threats, intrusions and malware detection exploiting ML methods
  • Security architectures for heterogeneous 5G and beyond mobile networks
  • Trusted computing and trustworthy computing for 5G and beyond mobile technologies
  • Orchestration of SDN or NFV for securing 5G mobile applications
  • Data security for 5G device-to-device (D2D) communications

Publication

All registered papers will be submitted for publishing by Springer and made available through SpringerLink Digital Library.

Proceedings will be submitted for inclusion in leading indexing services, such as Web of Science, EI Engineering Index (Compendex and Inspec databases), DBLP, EU Digital Library, Google Scholar, IO-Port, MathSciNet, Scopus, Zentralblatt MATH. Selected papers of this workshop will be invited to submit an extended version to:

Community Review

Community Review is a service offered to Program Committees and submitting Authors of all EAI conferences designed to improve the speed and the quality of the review process.

Abstracts of all authors who opt in to Community Review during submission will be published and available for Bidding here.

Learn more about the Community Review process

Submission

We invite workshop participation through contributions that respond to one or more of the mentioned research questions in (6-11) pages. Short papers should be submitted through EAI ‘Confy+‘ system, and have to comply with the Springer format (see Author’s kit section).

Important Dates

Paper Submission deadline
10 July 2022
Notification deadline
27 August 2022
Camera-ready deadline
27 September 2022

Workshop Chairs

Dr. Shuai Zhao
University of York, UK

TPC Members: TBD