| 书目名称 | Robust Network Compressive Sensing |
| 编辑 | Guangtao Xue,Yi-Chao Chen,Minglu Li |
| 视频video | |
| 概述 | Provides anomaly detection technologies for various networking data from Internet.Introduces the theory and assumption behind the compressive sensing technology.Covers the theory of compressive sensin |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | .This book investigates compressive sensing techniques to provide a robust and general framework for network data analytics. The goal is to introduce a compressive sensing framework for missing data interpolation, anomaly detection, data segmentation and activity recognition, and to demonstrate its benefits. Chapter 1 introduces compressive sensing, including its definition, limitation, and how it supports different network analysis applications. Chapter 2 demonstrates the feasibility of compressive sensing in network analytics, the authors we apply it to detect anomalies in the customer care call dataset from a Tier 1 ISP in the United States. A regression-based model is applied to find the relationship between calls and events. The authors illustrate that compressive sensing is effective in identifying important factors and can leverage the low-rank structure and temporal stability to improve the detection accuracy. Chapter 3 discusses that there are several challenges in applying compressive sensing to real-world data. Understanding the reasons behind the challenges is important for designing methods and mitigating their impact. The authors analyze a wide range of real-world tr |
| 出版日期 | Book 2022 |
| 关键词 | Network analytics; Anomaly detection; Compressive sensing; Activity recognition; Data-driven synchroniza |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-16829-1 |
| isbn_softcover | 978-3-031-16828-4 |
| isbn_ebook | 978-3-031-16829-1Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s), under exclusive license to Springer Nature Switzerland AG 2022 |
发表于 2025-3-21 16:43:39
