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A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management

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Abstract

Modern networks and systems pose many challenges to traditional management approaches. Not only the number of devices and the volume of network traffic are increasing exponentially, but also new network protocols and technologies require new techniques and strategies for monitoring controlling and managing up and coming networks and systems. Moreover, machine learning has recently found its successful applications in many fields due to its capability to learn from data to automatically infer patterns for network analytics. Thus, the deployment of machine learning in network and system management has become imminent. This work provides a review of the applications of machine learning in network and system management. Based on this review, we aim to present the current opportunities and challenges in and highlight the need for dependable, reliable and secure machine learning for network and system management.

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Acknowledgements

This research is supported by Natural Science and Engineering Research Council of Canada (NSERC). Duc C. Le gratefully acknowledges the supports of the Killam Trusts and the province of Nova Scotia. The research is conducted as part of the Dalhousie NIMS Lab at: https://projects.cs.dal.ca/projectx/. The authors would like to thank the anonymous reviewers.

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    Duc C. Le & Nur Zincir-Heywood

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Correspondence to Nur Zincir-Heywood.

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Le, D.C., Zincir-Heywood, N. A Frontier: Dependable, Reliable and Secure Machine Learning for Network/System Management. J Netw Syst Manage 28, 827–849 (2020). https://doi.org/10.1007/s10922-020-09512-5

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