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Improving Managed Network Services using Cooperative Synthetic Data Augmentation.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Improving Managed Network Services using Cooperative Synthetic Data Augmentation./
作者:
Jin, Minhao.
面頁冊數:
1 online resource (44 pages)
附註:
Source: Masters Abstracts International, Volume: 84-06.
Contained By:
Masters Abstracts International84-06.
標題:
Computer science. -
電子資源:
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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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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