Applied Cloud Deep Semantic Recognition: Advanced Anomaly Detection

  • 出版商: Auerbach Publication
  • 出版日期: 2018-03-15
  • 售價: $4,500
  • 貴賓價: 9.5$4,275
  • 語言: 英文
  • 頁數: 202
  • 裝訂: Hardcover
  • ISBN: 1138302228
  • ISBN-13: 9781138302228
  • 相關分類: DeepLearning雲端運算
  • 立即出貨 (庫存 < 3)

商品描述

This book provides a comprehensive overview of the research on anomaly detection with respect to context and situational awareness that aim to get a better understanding of how context information influences anomaly detection. In each chapter, it identifies advanced anomaly detection and key assumptions, which are used by the model to differentiate between normal and anomalous behavior. When applying a given model to a particular application, the assumptions can be used as guidelines to assess the effectiveness of the model in that domain. Each chapter provides an advanced deep content understanding and anomaly detection algorithm, and then shows how the proposed approach is deviating of the basic techniques. Further, for each chapter, it describes the advantages and disadvantages of the algorithm. The final chapters provide a discussion on the computational complexity of the models and graph computational frameworks such as Google Tensorflow and H2O because it is an important issue in real application domains. This book provides a better understanding of the different directions in which researchers has been done on deep semantic analysis and situational assessment using deep learning for anomalous detection, and how methods developed in one area can be applied in applications in other domains. This book seeks to provide both cyber analytics practitioners and researchers an up-to-date and advanced knowledge in cloud based frameworks for deep semantic analysis and advanced anomaly detection using cognitive and artificial intelligence (AI) models.

商品描述(中文翻譯)

本書提供了對於異常檢測的研究的全面概述,重點在於上下文和情境感知,旨在更好地理解上下文信息如何影響異常檢測。在每一章中,它確定了先進的異常檢測和關鍵假設,這些假設被模型用於區分正常和異常行為。當將給定的模型應用於特定應用時,這些假設可以用作評估該領域中模型有效性的指南。每一章提供了先進的深度內容理解和異常檢測算法,然後展示了所提出方法與基本技術的差異。此外,對於每一章,它描述了該算法的優點和缺點。最後幾章討論了模型的計算複雜性以及諸如Google Tensorflow和H2O等圖計算框架,因為這在實際應用領域中是一個重要問題。本書提供了對於使用深度學習進行深度語義分析和情境評估進行異常檢測的不同方向的研究的更好理解,以及在一個領域中開發的方法如何應用於其他領域的應用。本書旨在為網絡分析從業人員和研究人員提供關於基於雲的框架的深度語義分析和先進異常檢測的最新和先進知識,並使用認知和人工智能(AI)模型。