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Computer Science - Communication Networks | CCF Transactions on Networking (Press)

CCF Transactions on Networking

CCF Transactions on Networking

Editor-in-Chief: J. Wu; K.K. Ramakrishnan

ISSN: 2520-8462 (print version)
ISSN: 2520-8470 (electronic version)

Journal no. 42045

Special Issue on AI Security in Emerging Networking Contexts

Submission Deadline: 16 August 2019
Initial Publication Date: 15 December 2019
Guest Editors
Elisa Bertino, Purdue University, USA
Robert Deng, Singapore Management University, Singapore
Qi Li, Tsinghua University, China
Kui Ren, Zhejiang University, China
Cong Wang, City University of Hong Kong, Hong Kong
Artificial intelligence and machine learning have been rapidly gaining ground in many different fields. Following this trend, network community has been also seeking to apply advanced AI/ML techniques in emerging networking contexts, such as NFV, edge computing, data center networking, and more recently decentralized ledger mechanisms.
This Special Issue focuses on security and privacy issues in the intersection of AI with networking. Prior studies have shown that ML technologies have great potential for cyber defense, such as anomalous behavior detection and botnet detection. However, in emerging networking contexts, there are still many challenging obstacles yet to be identified and addressed. For example, in edge computing, the widely‐deployed edge devices can be equipped with ML‐enabled defense techniques for advanced cyber threats. Yet, the relatively limited resources, as well as the lack of global information at edges inevitably bring in complex design challenges in practice.
Besides, ML technologies are vulnerable to various known attacks, such as easily fooled/poisoned by adversarial examples. These potential vulnerabilities become severer in emerging networking contexts, and thus inspire new frontiers. For example, while edge computing utilizes the federated learning framework to enable decentralized ML at edges, it opens wide attack surface for compromised nodes, and consequently, requires domain- specific designs. Moreover, recent efforts have witnessed invaluable uses of decentralized ledger systems (i.e., blockchains) and AI. This hot direction also looks for secure solutions towards trustworthy AI applications built upon blockchain systems.
This Special Issue aims to bring together academic and industry researchers from the networking, AI, and security communities to jointly propose blue prints towards trustworthy AI for/in networking.
We encourage submissions on topics including, but not limited to, the following:
AI-enabled security networking applications
- DDoS prevention, detection, and mitigation
- Botnet detection
- Intrusion detection and response
- Anomalous behavior detection
- Data anonymization/de-anonymization
- Big data analytics for network security
- Security-related AI and networking problems
- Verifiable network middleboxes
- Hardware-based security components in networking
- Multiparty computation in decentralized networks
- Privacy-preservation in AI-enabled network applications
- Blockchain-based decentralized AI applications
- Proactive defence mechanisms for AI-based attacks
- Robust designs resilient to adversarial attacks
Manuscript Preparation
Papers must contain original contributions and are not currently under review by a conference or a journal. In addition, the manuscripts should be prepared in accordance with the journal standards. Instructions for the authors are available online at: https://link.springer.com/journal/42045
Manuscript Submissions
Manuscripts should be submitted online at: http://www.editorialmanager.com/cton/default.aspx
The cover letter should report the following statement: “This paper is submitted for possible publication in the Special Issue on AI Security in Emerging Networking Contexts.”
All manuscripts will be subjected to the peer review process. If you have any questions, please email to the Journal Editorial Office Assistant Neeraj Poduval at neeraj.poduval@springer.com
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  • Aims and Scope

    Aims and Scope


    CCF Transactions on Networking (CCF TON) disseminates valuable new research on networking to global researchers and reports on the latest research findings in the covered areas. It provides a communication and publication platform for all aspects of computer networks, ranging from fundamental theories and novel applications to emerging interdisciplinary research fronts. The journal publishes original, high-quality and previously unpublished research papers, review and survey articles, research commentaries and critical reviews. CCF TON welcomes contributions from researchers, professionals and industrial practitioners.

    This journal is published on behalf of the China Computer Federation (CCF), and it is powered by the CCF technical committees on Internet, Network and Data Communication and Internet of Things.

    Specific areas of interest include but not limited to:

     (1) Network theory and design

    ·         Network modeling and analysis, architecture and design of computer networks;

    ·         Communications protocol design, analysis and implementations of network systems;

    ·         Cyber-physical systems;

    ·         Data center networking;

    ·         Game theory in networking;

    ·         Energy efficiency in next generation networks

    ·         Internet-of-Things;

    ·         Wireless, sensor and optical networks;

    ·         Software defined networking;

    ·         Network economics and pricing.

    (2) Applications

    ·         Crowdsourcing and crowdsensing systems;

    ·         Big data analytics, data processing for networks;

    ·         Network management;

    ·         Distributed systems;

    ·         Cloud computing;

    ·         Smart grids;

    ·         Social computing and networking;

    ·         Network function virtualization.

    (3) Multidisciplinary

    ·         Cyber security (including applied cryptography, network security and application security);

    ·         New techniques for communication networks, inspired by multidisciplinary studies.

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