A NetAI Manifesto (Part II): Less Hubris, more Humility
Pages 109 - 111
Abstract
The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving real-world network security and performance problems (NetAI, for short) has generated great excitement among networking researchers. However, network operators have remained very reluctant when it comes to deploying NetAIbased solutions in their production networks. In Part I of this manifesto, we argue that to gain the operators' trust, researchers will have to pursue a more scientific approach towards NetAI than in the past that endeavors the development of explainable and generalizable learning models. In this paper, we go one step further and posit that this "opening up of NetAI research" will require that the largely self-assured hubris about NetAI gives way to a healthy dose humility. Rather than continuing to extol the virtues and "magic" of black-box models that largely obfuscate the critical role of the utilized data play in training these models, concerted research efforts will be needed to design NetAI-driven agents or systems that can be expected to perform well when deployed in production settings and are also required to exhibit strong robustness properties when faced with ambiguous situations and real-world uncertainties. We describe one such effort that is aimed at developing a new ML pipeline for generating trained models that strive to meet these expectations and requirements.
References
[1]
W. Willinger et al.A NetAI Manifest (Part I): Less Explorimentation, More Science. Performonce Evaluation Review, this issue (2023).
[2]
D. D. Woods. Automation Surprises. In: Joint Cognitive Systems: Patterns in Cognitive Systems Engineering, 113--142, Taylor & Francis, 2006.
[3]
J. M. Bradshaw et al.The Seven Deadly Myths of 'Autonomous Systems'. IEEE Intelligent Systems 28(3), 2--8, 2013.
[4]
D. L. Alderson and J. C. Doyle. Contrasting views of complexity and their implications for network-centric infrastructures. IEEE SMC-Part A, 40(4), 839--852 (2010).
[5]
D. D. Woods. The Risks of Autonomy: Doyle's Catch. Journal of Cognitive Engineering and Decision Making, 10(2), 131--133 (2016).
[6]
R. Beltiukov, W. Guo, A. Gupta, and W. Willinger. In Search of netUnicorn: A Data-Collection Platform to Develop Generalizable ML Models for Network Security Problems. https://arxiv.org/abs/2306.08853 (2023).
[7]
A. S. Jacobs et al.AI/ML for network security: The emperor has no clothes. Proc. ACM CCS'22 (2022).
Recommendations
A NetAI Manifesto (Part I): Less Explorimentation, More Science
The application of the latest techniques from artificial intelligence (AI) and machine learning (ML) to improve and automate the decision-making required for solving realworld network security and performance problems (NetAI, for short) has generated ...
Comments
Information & Contributors
Information
Published In

Copyright © 2023 Copyright is held by the owner/author(s).
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 02 October 2023
Published in SIGMETRICS Volume 51, Issue 2
Check for updates
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 57Total Downloads
- Downloads (Last 12 months)25
- Downloads (Last 6 weeks)4
Reflects downloads up to 17 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in