The International Journal of Computer and Telecommunications Networking

Computer Networks is an international, archival journal providing a publication vehicle for complete coverage of all topics of interest to those involved in the computer communications networking area. The audience includes researchers, managers and operators of networks as well as designers and implementors. The Editorial Board will consider any material for publication that is of interest to those groups.

SUBJECT COVERAGE

The topics covered by the journal but not limited to these are:

1. Communication Network Architectures:
New design contributions on Local Area Networks (LANs), Metropolitan Area Networks (MANs), Wide Area Networks (WANs) including Wired, Wireless, Mobile, Cellular, Sensor, Optical, IP, ATM, and other related network technologies, as well as new switching technologies and the integration of various networking paradigms.

2. Communication Network Protocols:
New design contributions on all protocol layers except the Physical Layer, considering all types of networks mentioned above and their performance evaluation; novel protocols, methods and algorithms related to, e.g., medium access control, error control, routing, resource discovery, multicasting, congestion and flow control, scheduling, multimedia quality of service, as well as protocol specification, testing and verification.

3. Network Services and Applications:
Web, Web caching, Web performance, Middleware and operating system support for all types of networking, electronic commerce, quality of service, new adaptive applications, and multimedia services.

4. Network Security and Privacy:
Security protocols, authentication, denial of service, anonymity, smartcards, intrusion detection, key management, viruses and other malicious codes, information flow, data integrity, mobile code and agent security.

5. Network Operation and Management:
Including network pricing, network system software, quality of service, signaling protocols, mobility management, power management and power control algorithms, network planning, network dimensioning, network reliability, network performance measurements, network modeling and analysis, and overall system management.

6. Discrete Algorithms and Discrete Modeling
Algorithmic and discrete aspects in the context of computer networking as well as mobile and wireless computing and communications. Fostering cooperation among practitioners and theoreticians in this field.TYPES OF CONTRIBUTIONS CONSIDERED

The primary purpose of the journal is to publish original and complete papers covering a specific topic or project in the above mentioned areas in sufficient detail and depth to be of practical use to interested readers. The readers should benefit from the novel solutions and analyses presented in the papers. Enhanced, extended versions of quality papers presented at conferences or workshops can be submitted to our journal for review. Note that papers which were already published with the same contents or simultaneous submission of the same paper to other journals or conferences will not be considered for publication in our journal and will be immediately rejected.

Dataset Articles. Computer Networks also publishes micro-articles that describe open datasets available in a redacted and organized way. The purpose is for researchers to easily share and reuse each other's datasets by publishing data articles that:

(i) Describe collected data in detail, facilitating reproducibility of experiments and improvements over proposed techniques, thus promoting rigorous experimentation and data analysis.
(ii) Describe tools developed to collect, analyze, and visualize data.

Open-Source Software Articles. Computer Networks additionally publishes micro-articles that describe open source software that has been used to obtain scholarly results in the area of computer networks. This may include articles describing discrete-event or other simulators, emulation tools, software implementations of networking and communication functionalities and protocols, standard implementations, monitoring tools, among others.

Special Issue on Digital Twins for Open, Programmable, and AI-driven Wireless Networks
Submission Date: 2024-10-15

A Digital Twin (DT) serves as a highly realistic digital version of real-world entities, assists the development and evaluation of complex systems such as wireless networks, offers real-time insights into such systems behavior and performance, and enables optimized control of the twinned physical entities. DTs serve as safe environments for testing different setups of real systems without affecting their performance or user experience. Complementing this, Artificial Intelligence (AI) augments these insights with advanced analytics and decision-making capabilities, enabling automated configuration and optimization, and self-healing of complicated systems such as wireless networks. The high-fidelity DTs also make it easier to collect data for training AI models, offering flexibility and automation that is hard to achieve in the physical world.This Special Issue delves into the realm of enabling DT-based techniques for open, programmable, and AI-driven wireless networks in 5G and beyond. With a spotlight on AI and end-to-end virtualization through DTs, the goal is to elucidate the potential, challenges, and future trajectory of this integration. The convergence of DTs and AI not only facilitates modeling and simulation but also paves the way for dynamic network environments where seamless interactions between physical and virtual entities thrive.Both theoretical and experimental contributions on DT and AI in wireless are welcome. The scope includes, but is not limited to: DTs for Open Radio Access Network (Open RAN); Methodologies for testing and integrating DTs within Open RAN ecosystems, ensuring interoperability, standardization, and security; DT-assisted 6G networks; DTs for autonomous and UAV networks; AI/ML for DT-assisted wireless networks; Security and privacy in DT-assisted 6G networks; Empirical inquiries, case studies, industry reports, taxonomies, and high-quality surveys of DTs for wireless networks; DT architectures for networked systems.

Guest editors:

Pedram Johari, PhD Northeastern University, Boston, MA, USAp.johari@northeastern.edu(wireless telecommunications, digital twins, Open RAN, experimental testbed platforms)

Mohammad Shojafar, PhDUniversity of Surrey, Guildford, UKm.shojafar@surrey.ac.uk(DLT and Blockchain applied in the network, Cloud and Fog Networks and their security and privacy issues, 5G/6G distributed and networked systems, Security and Privacy of 6G Open-RAN and Future Networks)

Ahmed Alkhateeb, PhDArizona State University, Tempe, AZ, USAalkhateeb@asu.edu(Wireless communications, signal processing, machine learning)

Istvan David, PhDMcMaster University, Hamilton, Canadaistvan.david@mcmaster.ca(digital twins, cyber-physical systems, model-driven engineering, simulation, sustainable systems)

Vishal Sharma, PhDQueen's University Belfast (QUB), Belfast, UKv.sharma@qub.ac.uk(Network Security, Digital Twin, Cyber Defence, DLT, UAVs)

Marie-José Montpetit, Ing. PhDMcGill University, Montreal, Canadamarie-jose.montpetit@mcgill.ca(Distributed systems, networking, federated AI)

Manuscript submission information:

The journal's submission platform (EditorialManager®) will be available for receiving submissions to this Special Issue from May 31st, 2024. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: DTWN” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Computer Networks | Journal | ScienceDirect.com by Elsevier.

Timeline:

Submission Open Date *31/05/2024
Final Manuscript Submission Deadline *15/10/2024
Editorial Acceptance Deadline *28/02/2025

Special Issue on Generative and Explainable Artificial Intelligence for Internet Traffic and Architectures
Submission Date: 2024-12-01

This Special Issue (SI) delves into the transformative impact of Artificial Intelligence (AI) on Internet traffic analysis and network management, focusing on generative AI and explainable AI techniques. Key topics include the use of generative AI for creating realistic and diverse traffic datasets, crucial for training robust AI-driven models used in intrusion detection and network optimization. AI-driven automatic network configuration is also explored, highlighting how AI can dynamically adjust network settings to enhance performance and security. On the other hand, given the black-box nature of such data-driven techniques, the SI emphasizes explainable AI techniques to ensure transparency and trust, making AI decisions understandable and actionable to network operators.

The SI encompasses a wide range of themes, including AI-driven generation of standardized traffic datasets, network management aided by generative AI, and interpretable and trustworthy AI solutions for Internet traffic analysis. Additionally, real-world applications and case studies are presented, demonstrating the practical utility of generative and explainable AI in network security and performance optimization.

Through detailed research and practical insights, this SI highlights innovative AI approaches addressing the complex challenges of modern networks, paving the way for future advancements in the field.

Guest editors:

Antonio Montieri, PhD
University of Napoli Federico II, Napoli, Italy

Danilo Giordano, PhD
Politecnico di Torino, Torino, Italy

Claudio Fiandrino, PhD
IMDEA Networks, Leganes, Spain

Jonatan Krolikowski, PhD
Huawei Technologies France SAS, Boulogne Billancourt, France

Special issue information:

In Internet traffic analysis, the advent of Artificial Intelligence (AI) has marked a significant paradigm shift. With the proliferation of Internet-connected devices and the exponential growth of traffic data, traditional traffic analysis methods are struggling to cope with modern networks' volume and complexity. Moreover, the dynamic nature of Internet traffic and the emergence of sophisticated cyber threats further exacerbate the challenges faced by network operators and cybersecurity professionals. In response, there is a pressing need for advanced analytical tools to provide accurate Internet traffic “visibility”, enable actionable insights into traffic behavior, identify anomalies and intrusions, and ultimately enhance network security and performance.

On the other hand, the collection, segmentation, and labeling of traffic datasets are cumbersome processes, often requiring human expertise. Factors like the dynamic nature of traffic, privacy concerns, and the limited samples of certain traffic types (e.g., network attacks and IoT devices) further challenge data collection. Moreover, while data-driven techniques have the potential for outstanding performance and adaptability, they often operate as black boxes, making it difficult to understand their behavior, improve their performance, or protect them from attacks. This limits the interpretability and trust in these methods, affecting their practical use.

The integration of generative and explainable AI presents a promising avenue for addressing these challenges. By harnessing the power of AI to generate realistic traffic data and provide interpretable insights, researchers and practitioners can overcome the limitations of traditional traffic analysis methods. Generative AI enables the creation of diverse and representative traffic datasets, facilitating the training of AI-driven models for intrusion detection and network optimization. Meanwhile, explainable AI enhances the transparency and trustworthiness of AI-driven traffic analysis, enabling network operators to understand and interpret their decisions.

This Special Issue (SI) aims to delve into the methodological, technical, and practical aspects of leveraging generative and explainable AI for Internet traffic analysis and network architectures. By focusing on these cutting-edge topics, we seek to provide a platform for researchers and practitioners to explore innovative approaches, share insights, and advance state of the art. The SI covers a wide range of themes, including AI-driven generation of standardized traffic datasets, network management aided by generative AI, interpretable and trustworthy AI solutions for Internet traffic analysis, and real-world applications of generative and explainable AI in network optimization and security. Considering the specificity and value of the topics to the community, papers with a high degree of reproducibility are encouraged, be it through the publication of source code and/or raw data.

Manuscript submission information:

The journal's submission platform (Editorial Manager®) will be available for receiving submissions to this Special Issue from July 1st, 2024. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: GenXAI for Internet” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Computer Networks | Journal | ScienceDirect.com by Elsevier.

Timeline:

Submission Open Date *01/07/2024
Final Manuscript Submission Deadline *01/12/2024
Editorial Acceptance Deadline *01/03/2025

Keywords:

Generative AI, Explainable AI, AI-driven Network Traffic Analysis, Internet Traffic Analysis, Network Architectures, Network Management, Network Security, Network Analytics