Network Traffic Analytics in the Era of AI and SDN

  in Special Issue   Posted on October 19, 2020

Information for the Special Issue

Submission Deadline: Sun 31 Jan 2021
Journal Impact Factor : 3.111
Journal Name : Computer Networks
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

In recent years, world-wide users have enjoyed diverse desktop/mobile applications driven by rich information, which is delivered by the emerging networking technologies such as Software Defined Networking, 5G, and IoT. As such applications proliferate, however, it is increasingly challenging for network operators to cope with the complexity of network applications, dynamics of network traffic and non-stop security threats to network infrastructure. One of the core elements to secure and scalable network management and operation is the in-depth, non-intrusive and timely understanding of network traffic flowing through the network infrastructure. Albeit the growing number of AI and machine learning based studies on network traffic analytics, there remain many open questions due to the fluidity of the network flows, variety of objectives and security and privacy considerations. For example, there is not a comprehensive, open dataset for benchmarking the proposed algorithms and designs.

This special issue aims to provide a venue for the community to present and discuss the latest advances in network traffic analytics with an emphasis on novel machine learning based approaches and/or Software Defined Networking (SDN) environment. The analytics is broadly defined, including but not limited to flow classification, volume predication, measurement driven management, and covers the full life cycle of network flows. Given the recent development of AI driven applications, we see this an excellent opportunity for networking researchers to interact with ML/AI community to foster new knowledge and advance the state of art. We hope this special issue will catalyze the research and development of novel methods for network traffic analytics with rich datasets, vigorous discussions, new directions and fruitful collaborations.

Relevant topics/areas include but are not limited to

  • Network traffic analysis using machine learning methods
  • Malware detection using machine learning methods
  • Deep learning-based network traffic analytics
  • Encrypted network traffic analysis
  • Privacy preserving network traffic analysis
  • SDN support for network traffic analytics and measurement
  • Benchmarking and datasets for network traffic analysis
  • Comparison and validation of AI tools in networking domain
  • Platforms and environments enabling realistic network traffic capturing and analysis
  • Real-time network traffic analytics in uCPE and SD-WAN
  • Network traffic analysis in embedded systems with limited computing and memory resources
  • Hardware and software solutions for accelerated, high throughput network traffic analytics

Important Dates:

Deadline of initial submission: Jan 31, 2021

Completion of first round review: March 31, 2021

Deadline for revised manuscripts: May 31, 2021

Completion of review and final decision: July 1, 2021

Publication: Sept 1, 2021.

Lead Guest Editors:

Yan Luo, University of Massachusetts Lowell (

Tong Zhang, Intel Corporation

Guest Editors:

Hongxin Hu, Clemson University

Franck Le, IBM

Peilong Li, Elizabethtown College

Richard Yang, Yale University

Other Special Issues on this journal

Closed Special Issues