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CALL FOR PAPERS
Special Issue on Generative and Explainable AI
for Internet Traffic and Network Architectures
Elsevier Computer Networks
(EXTENDED Submission deadline: February 14, 2025)
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We are pleased to announce a call for papers for a special issue of
Elsevier Computer Networks journal, focusing on the transformative potential
of generative and explainable AI in Internet traffic analysis and network
architectures. As Internet-connected devices multiply and traffic data grows
exponentially, traditional methods are increasingly challenged. This special
issue aims to highlight how generative AI can synthesize realistic traffic data,
automate network configurations, and enhance security measures. Additionally,
explainable AI can provide deeper insights into network behaviors, improving
transparency, trust, and overall network performance.
We invite you to contribute to this pioneering special issue and lead the
advancement of AI-driven innovations in Internet traffic analysis and network
architectures.
Key Topics of Interest include but are not limited to the following:
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- Generative AI methods to synthesize realistic and diverse traffic data
- Automatic network configuration and management utilizing Generative AI
- Applications of Large Language Models (LLMs) in network traffic generation
- Prompt Engineering for LLMs in network traffic analysis, management, and security
- Generative AI for enhancing network security and intrusion detection
- Assessing the robustness and reliability of Generative AI in network
management, including standardized benchmarks and datasets
- Explainable AI techniques for network traffic analysis and management tools
- Human-in-the-loop AI and the integration of interpretability into AI-driven
traffic analysis
- Ensuring fairness, accountability, and transparency in AI applications for
networking
- Real-world applications and case studies showcasing Generative and Explainable
AI in network traffic analysis, management, and security
- Bridging the gap between network data explanation and actionable
interpretability
- Techniques for improving the trust and practical use of data-driven network
analysis methods
Guest Editors:
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- Antonio Montieri, PhD - Università degli Studi di Napoli Federico II, Napoli, Italy
- Danilo Giordano, PhD - Politecnico di Torino, Torino, Italy
- Claudio Fiandrino, PhD - IMDEA Networks Institute, Madrid, Spain
- Jonatan Krolikowski, PhD - Huawei Technologies France SAS, Boulogne Billancourt, France
(jonatan.krolikowski@huawei.com)
Important Dates:
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- Submission Open Date: July 1, 2024
- Final Manuscript Submission Deadline: February 14, 2025 (EXTENDED)
- Editorial Acceptance Deadline: May 15, 2025
Manuscript Submission Information:
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The journal's submission platform
(https://www.editorialmanager.com/comnet/default.aspx) is available for
receiving submissions to this Special Issue from July 1st, 2024. Authors are
advised to follow the Guide for Authors to prepare their manuscripts and select
the article type “VSI: GenXAI for Internet” when submitting online. More
information about the Special Issue, the Guide for Authors, and the submission
portal are available at the following link:
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SPECIAL ISSUE DETAILS
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In the realm of 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 the sheer volume
and complexity of modern networks. Moreover, the dynamic nature of Internet
traffic patterns 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
that can 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 experts to guide the different
stages. Additionally, factors like the dynamic nature of traffic, privacy concerns,
and the limited samples of certain traffic types (e.g., network attacks, IoT
devices) further challenge data collection. Moreover, while data-driven
techniques have the potential for outstanding performance and adaptability, they
often operate as black-box systems, making it difficult to understand their
behavior, improve their performance, or protect them from potential attacks.
This limits the interpretability and trust in these methods, affecting their
practical use.
The integration of generative and explainable AI techniques 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 models enable the creation of diverse and representative
traffic datasets, facilitating the training of AI-driven models for intrusion
detection and network optimization. Meanwhile, explainable AI techniques enhance
the transparency and trustworthiness of AI-driven traffic analysis, enabling
network operators to understand and interpret the decisions made by AI methods.
This special issue 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 special issue will encompass 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.
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