Aim and Scope
The clear understanding of the processes occurring in networks is paramount for multiple stakeholders, including network operators, who aim at the full visibility required by both network management and security. Accordingly, devising a suitable set of tools for performing network traffic analytics is of the utmost importance to understand its peculiarities, predict its characteristics, enforce traffic engineering, perform network planning and provisioning, manage the QoS, profile user activities, identify anomalies, emulate real traffic for testing purposes, etc.
However, this process is challenged by the nature of the traffic traversing today's networks which is impacted by the way users behave, interact, and access the network. In fact, operators have experienced in the last years tremendous growth of the traffic to be managed in their networks, mostly generated by mobile and IoT devices, according to the latest reports from big network players it is forecasted that mobile subscriptions will reach around 9 billions by 2025, corresponding to mobile data traffic of 160 exabytes per month. This phenomenon exacerbates the need for accurate characterization, classification, modeling, and predictability of network traffic generated by a plethora of heterogeneous devices at different degrees of granularity. In fact, the implementation of effective approaches for fine-grained traffic analytics must overcome several challenges. One is the broad adoption of encrypted protocols, e.g. Transport Layer Security (TLS), which currently represents the majority of traffic. Also, network traffic is increasingly becoming an extremely complex and dynamic phenomenon, leading to wildly-different and complex fingerprints.
On the one hand, global network solutions providers tend to prefer the applicability of model-based approaches due to the interpretability. On the other hand, ML/DL techniques have sought increasing attention due to their ability in solving complex tasks with data-driven procedures. Unfortunately, the data-driven characteristics exacerbate the difficulties in designing and evaluating the latter techniques when applied to traffic analytics. These issues are the more worrying the less interpretable the ML/DL technique is, with the currently-popular neural networks (in their deep learning "flavor'") representing the most exposed ones.
Finally, the availability of high-quality and up-to-date datasets and ground truths for needed analyses is quite limited, given both the variety and the dynamicity of services/apps generating traffic, and the privacy concerns implied in the collection and sharing of such data. Hence, the design of network traffic analytic tools remains an open and hot challenge, and innovative approaches are needed to ensure satisfactory traffic visibility while having theoretically-solid and easily-interpretable views of the traffic.
Accordingly, the aim of this Workshop is to provide an overview on recent advances and challenges in network traffic analytics. In line with the interesting research efforts in the field of ML and DL, the workshop solicits contributions related to the application of these techniques in critical networking scenarios, such as IoT or Industry 4.0 systems, with a focus on horizontal tasks such as traffic modeling, classification, prediction or more vertical applications of analytics in challenging scenarios such as Fog and Cloud. This workshop offers the opportunity for researchers and practitioners to identify new promising research directions as well as to publish recent advances in this area.
- Workshop paper submission deadline:
25th April, 202110th May, 2021
- Notification of workshop paper acceptance:
May 17th, 2021May 24th, 2021
- Submission of camera-ready workshop papers due: June 15th, 2021
- Workshop early registration: To Be Defined
University of Napoli
University of Napoli
University of Napoli
Technical Program Committee (To Be Defined)
- Alessandro Finamore, HUAWEI Technology, France
- Antonio Pescapè, University of Napoli Federico II
- Esteban Carisimo, Facultad de Ingenierìa, Universidad de Buenos Aires
- Giuseppe Aceto, University of Napoli Federico II
- Haiming Chen, Department of Computer Science, Ningbo Univerisity
- José Suárez-Varela, Barcelona Neural Networking center, Universitat Politècnica de Catalunya
- Michele Gucciardo, IMDEA Networks Institute
- Paul Patras, The University of Edinburgh
Note: if interested in being part of the TPC, please contact workshop organizers.
Call for Papers
The aim of this Workshop is to provide an overview on recent advances and challenges in network traffic analytics.
All IWNTA 2021 accepted papers will be published by Elsevier Science in the open-access Procedia Computer Science series on-line. Procedia Computer Science is hosted by Elsevier on www.Elsevier.com and on Elsevier content platform ScienceDirect ScienceDirect, and will be freely available worldwide. All papers in Procedia will be indexed by Scopus and by Thomson Reuters' Conference Proceeding Citation Index. All papers in Procedia will also be indexed by Scopus and Engineering Village (EI). This includes EI Compendex. Moreover, all accepted papers will be indexed in DBLP. The papers will contain linked references, XML versions and citable DOI numbers. You will be able to provide a hyperlink to all delegates and direct your conference website visitors to your proceedings.
The workshop seeks original completed and unpublished work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:
- Mobile and IoT traffic characterization
- AI-based approaches for traffic analysis
- Network anomaly and misuse detection
- Machine and Deep Learning approaches for network analytics
- Model-based (statistical) approaches for network analytics
- Characterization of open network traffic data and reproducibility
- Interpretability in ML-based network analytics tool
- Vertical use cases for network analytics including Mobile Traffic, IoT, Crypto-mining applications, anonymity tools, Industry 4.0
- Augmented Reality and Virtual Reality
- Applications of network analytics in fog and cloud scenarios
- Prototypical implementation of network traffic analysis tools
- Artificial Intelligence for Industry 4.0
All papers should be submitted via Easy Chair through >>this link<<. Authors are required to submit fully formatted, original papers (in PDF format), and should be no more than 6 pages (including tables, figures, and references) formatted according to the guidelines of Procedia Computer Science (MS Word Template, Latex Template, Template Generic). Papers exceeding 6 pages will not be accepted by Easy Chair. At least one author of each accepted paper is required to register to the workshop. All accepted and registered papers will be published in the Elsevier proceedings. Given the current pandemic situation, in-person authors' participation is not mandatory.
The joint MobiSPC 2021 conference will be held in-person at the Park Inn Radisson Hotel, Leuven, Belgium. Please refer to the Conference Venue and Accommodation on the MobiSPC 2021 website for any participating information including conference venue and travel. However, given the current pandemic situation, the authors' participation is not mandatory. All accepted papers will be published in the Elsevier proceedings.