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Improving Managed Network Services using Cooperative Synthetic Data Augmentation.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Improving Managed Network Services using Cooperative Synthetic Data Augmentation./
Author:	Jin, Minhao.
Description:	1 online resource (44 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-06.
Contained By:	Masters Abstracts International84-06.
Subject:	Computer science. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30242386click for full text (PQDT)
ISBN:	9798363516207

Improving Managed Network Services using Cooperative Synthetic Data Augmentation.
Jin, Minhao.

Improving Managed Network Services using Cooperative Synthetic Data Augmentation. - 1 online resource (44 pages)

Source: Masters Abstracts International, Volume: 84-06.

Thesis (M.S.)--Carnegie Mellon University, 2022.

Includes bibliographical references

Many managed service vendors in networking are adopting machine learning (ML) for many applications for their customers; e.g., anomaly detection, device ﬁngerprinting, and resource management. Today, the data for training is siloed across customers leading to sub-optimal performance. While there are emerging proposals (e.g., federated learning, multi-party computation) to enable cooperative learning, these are at odds with analysts need for data for model exploration and testing. In this thesis, we envision a novel use of synthetic data generated using Generative Adversarial Networks (GANs) to augment the performance of existing ML workﬂows. We formulate the cooperative data augmentation problem, identify the design space of options, and identify key research challenges. We demonstrate the preliminary promise under two settings: (1) traffic classiﬁcation and (2) novelty detection showing that our improved workﬂow can enhance the performance of ML models up to 58% in AUC score. We also identify limitations and discuss for future work.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2023

Mode of access: World Wide Web

ISBN: 9798363516207Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Managed network servicesIndex Terms--Genre/Form:

542853
Electronic books.

Improving Managed Network Services using Cooperative Synthetic Data Augmentation.
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500 $a Advisor: Sekar, Vyas.
502 $a Thesis (M.S.)--Carnegie Mellon University, 2022.
504 $a Includes bibliographical references
520 $a Many managed service vendors in networking are adopting machine learning (ML) for many applications for their customers; e.g., anomaly detection, device ﬁngerprinting, and resource management. Today, the data for training is siloed across customers leading to sub-optimal performance. While there are emerging proposals (e.g., federated learning, multi-party computation) to enable cooperative learning, these are at odds with analysts need for data for model exploration and testing. In this thesis, we envision a novel use of synthetic data generated using Generative Adversarial Networks (GANs) to augment the performance of existing ML workﬂows. We formulate the cooperative data augmentation problem, identify the design space of options, and identify key research challenges. We demonstrate the preliminary promise under two settings: (1) traffic classiﬁcation and (2) novelty detection showing that our improved workﬂow can enhance the performance of ML models up to 58% in AUC score. We also identify limitations and discuss for future work.
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538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 523869
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653 $a Machine Learning
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