2nd International Workshop on Network Intelligence (NI 2019)
“Machine Learning for Networking”
in conjunction with IEEE INFOCOM 2019
29 April – 2 May 2019 – Paris, France
Technically Sponsored by IEEE Communications Society,
Technical Committee on Cognitive Networking,
Technical Committee on Big Data, and
IEEE Network Intelligence Emerging Technologies initiative (IEEE NI ETI)
Network Intelligence considers the embedding of Artificial Intelligence (AI) in future networks to fasten service delivery and operations, leverage Quality of Experience (QoE) and guarantee service availability, also allowing better agility, resiliency, faster customization and security. This concept inherits the solid background of autonomic networking, cognitive management, and artificial intelligence. It is envisioned as mandatory to manage, pilot and operate the forthcoming network built upon SDN, NFV and cloud.
The main goal of the Network Intelligence Workshop is to present state-of-the-art research results and experience reports in the area of network intelligence, addressing topics such as artificial intelligence techniques and models for network and service management; smart service orchestration and delivery, dynamic Service Function Chaining, Intent and policy based management, centralized vs. distributed control of SDN/NFV based networks, analytics and big data approaches, knowledge creation and decision making. This workshop offers a timely venue for researchers and industry partners to present and discuss their latest results in Network Intelligence.
The main topic of this NI 2019 edition is “Machine Learning for Networking” which puts the attention on the particular application of machine learning tools to the optimization of next generation networks. Machine and deep learning techniques become increasingly popular and achieve remarkable success nowadays in many application domains, e.g., speech recognition, bioinformatics and computer vision. Machine learning is capable to exploit the hidden relationship from voluminous input data to complicated system outputs, especially for some advanced techniques, like the deep learning. Moreover, some other techniques, e.g., reinforcement learning, could further adapt the learning results in the new environments to evolve automatically. These features perfectly match the complex, dynamic and time-varying nature of today’s networking systems.
This workshop is supported by IEEE ComSoc Emerging Technical Initiative on Network Intelligence, technically sponsored by IEEE Communications Society, Technical Committee on Cognitive Networking, and Technical Committee on Big Data.
Topics of Interest
This workshop presents state-of-the-art research in machine learning for networking. Both theoretical and system papers will be considered, to present novel contributions in the field of machine learning, deep learning and, in general, network intelligent tools, including scalable analytic techniques and frameworks capable of collecting and analyzing both online and offline massive datasets, open issues related to the application of machine learning into communications and networking problems and to share new ideas and techniques for machine learning in communication systems and networks. The topics of interest include (but not limited to):
Papers must be submitted electronically as PDF files, formatted for 8.5×11-inch paper. The length of the paper must be no more than 6 pages in the IEEE double-column format (10-pt font). Papers should neither have been published elsewhere nor being currently under review by another conference or journal. The reviews will be single blind. At least one of the authors of every accepted paper must register and present the paper at the workshop. Accepted papers will be published in the combined INFOCOM 2019 Workshop proceedings and will be submitted to IEEE Xplore.
EDAS link for paper submission: http://edas.info/N25585
General Chairs (ordered by last name)
Technical Program Committee Chairs (ordered by last name)
NI Steering Committee Members (ordered by last name)
Series on Data Science and Artificial Intelligence for Communications
The objective of the Data Science and Artificial Intelligence for Communications Series of the IEEE Communications Magazine is to provide a forum across industry and academia to advance the development of network and system solutions using data science and artificial intelligence.
Innovations in artificial intelligence, machine learning, reinforcement learning and network data analytics introduce new opportunities in various areas, such as channel modeling and estimation, cognitive communications, interference alignment, mobility management, resource allocation, network control and management, network tomography, multi-agent systems, prioritization of network ultra-broadband deployments. These new analytic platforms will help revolutionize our networks and user experience. Through gathering, processing, learning and controlling the vast amounts of information in an intelligent manner future networks will enable unprecedented automation and optimization.
This Series solicits articles addressing numerous topics within its scope including, but not limited to, the following:
- All aspects of artificial intelligence, machine learning, reinforcement learning and data analytics aiming at enabling and enhancing next generation networks. The scope of issues that can be addressed includes both conventional measures such as traffic management, QoE, service quality, as well as future network behavior through intelligent services and applications.
- Methods, systems and infrastructure for the analysis of network, service traffic and user behavior for efficient and reliable design of networks, including deep learning and statistical methods for network tomography.
- Predictive analytics and artificial intelligence for network optimization, network security, network assurance, and data privacy and integrity. Diagnosis of network failures using analytics and AI.
- Automated communication infrastructure among smart machines and agents (including humans, e.g. speech and vision), and information fusion for automation and enablement of multi-agent systems.
- Communication and networking to facilitate smart data-centric applications
Manuscripts must be submitted through the magazine’s submissions Website at http://mc.manuscriptcentral.com/commag-ieee. You will need to register and then proceed to the author center. On the manuscript details page, please select Data Science and Artificial Intelligence for Communications Series from the drop-down menu. Manuscripts should be tutorial in nature and should not be under review for any other conference or journal. They should be written in a style comprehensible and accessible to readers outside the specialty of the article. Mathematical equations should not be used. For detailed submission guidelines please refer to the magazine website for the list of guidelines that must be followed by all submissions to the IEEE Communications Magazine: https://www.comsoc.org/commag/paper-submission-guidelines
Authors are encouraged to contact the Series Editor before submitting an article in order to ensure that the article will be appropriate for the Series. Papers can be submitted anytime during the year. They will receive a review process, and, if accepted, they will be published in the first slot available for this Series.
- Irena Atov, Microsoft, USA (firstname.lastname@example.org)
- Kwang-Cheng Chen, University of South Florida, USA (email@example.com)
- Shui Yu, University of Technology Sydney, Australia (firstname.lastname@example.org)
Applied Machine Learning Days (AMLD) – AI & Networking Track. More info coming soon.
Scope and Motivation:
The Cyber-Physical Systems (CPSs) have become very complex, more sophisticated, intelligent and autonomous. We cite as example of CPS smart grid in energy sector, smart factory and industry 4.0, intelligent transportation systems, healthcare and medical systems, and robotic systems. The CPSs offer very complex interaction between heterogeneous cyber and physical components; additionally to this complexity they are exposed to important disturbances due to unintentional and intentional events which lead the prediction of their behaviors (categorized as “Normal” or “Faulty”) a very difficult task. Meanwhile, cyber security for CPS is attracting the attention of research scientists in both industry and academia since the number of cyber-attacks have increased and their behaviors have become more sophisticated commonly known as zero-day threats.
Conventional cyber security mechanisms, such Intrusion Detection and Prevention Systems (IDS/IPS), and access control have not the capability to detect, prevent and block this category of cyber-attacks since the zero-day threats exhibit an unknown misbehavior that are not defined in signatures’ database of the security systems. Recently, a new era of cyber security mechanisms based on Artificial Intelligent (AI) are under development to protect the CPSs from these zero-day attacks. In the context of cyber security, the machine learning technologies are used to manage a huge amount of heterogeneous data that come from different sources of information with a goal of generating automatically different attacks patents and hence predict accurately the future attackers’ misbehavior. Meanwhile, game-theoretic approaches have been used in the context of cyber defense to solve the decision-making issues (i.e., the suspect device is an attacker or not) and attacks prediction. In decision-making issue, the cyber security game is used to study the interaction between the security agents (e.g., IDS and IPS) and their opponents (e.g. attackers) with a goal to determine the optimal decision making of security agent to classify the suspected opponent as attacker or not.
Preventing the occurrence of zero-day attacks requires the collaboration between different AI systems including machine learning and game theory, as well as security expert intervention. In fact, the involving of human intervention in the decision-making leads an improvement of attacks detection since the purpose of human-machine interaction is to reduce the number of false positives.
Another example to illustrate the migration of security solutions to use more intelligent principles and technologies, the Identity Management & Access control (IAM) which switch from a simple login/password checking to voice and facial recognition.
This Special Issue (SI) aims to bring together researchers from academic and industrial to share their visions of the AI application in cyber security context, present challenges and recent works and advances related to AI-based cyber security applied to CPSs. Potential topics include, but not limited to the following:
- Design and verification of AI-based security solutions,
- Impact of AI-based security solutions on CPS performances,
- Safety of AI-based security solutions.
- IDS/IPS based on machine learning,
- IDS/IPS based on deep and reinforcement learning,
- Cyber security game to protect the CPS,
- Authentication and Access Control,
- AI modeling for attack behavior,
- Attacks prediction based on machine learning and game theory,
- Human-machine interaction in the context of cyber security,
- Application of AI-based security in internet of things and transportation segments
- AI-based Solution for Physical layer security
- Manuscript Submission Deadline: May 1st 2019
- Initial Decision: August 1st, 2019
- Revised Manuscript Due: September 1st, 2019
- Decision Notification: November 1st, 2019
- Final Manuscript Due: December 1st, 2019
- Publication Date: May 2020
Manuscripts should conform to the standard format as indicated in the Information for Authors section of the Paper Submission Guidelines.
All manuscripts to be considered for publication must be submitted by the deadline through Manuscript Central. Select the “May 2020: Cyber Security based on Artificial Intelligence for Cyber-Physical Systems” topic from the drop-down menu of Topic/Series titles.
Guest Editors of the Special Issue: