Network Anomaly Detection: A Machine Learning Perspective (Hardcover)

Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita

  • 出版商: CRC
  • 出版日期: 2013-06-18
  • 售價: $5,540
  • 貴賓價: 9.5$5,263
  • 語言: 英文
  • 頁數: 366
  • 裝訂: Hardcover
  • ISBN: 1466582081
  • ISBN-13: 9781466582088
  • 相關分類: Machine Learning
  • 海外代購書籍(需單獨結帳)

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商品描述

With the rapid rise in the ubiquity and sophistication of Internet technology and the accompanying growth in the number of network attacks, network intrusion detection has become increasingly important. Anomaly-based network intrusion detection refers to finding exceptional or nonconforming patterns in network traffic data compared to normal behavior. Finding these anomalies has extensive applications in areas such as cyber security, credit card and insurance fraud detection, and military surveillance for enemy activities. Network Anomaly Detection: A Machine Learning Perspective presents machine learning techniques in depth to help you more effectively detect and counter network intrusion.

In this book, you’ll learn about:

  • Network anomalies and vulnerabilities at various layers
  • The pros and cons of various machine learning techniques and algorithms
  • A taxonomy of attacks based on their characteristics and behavior
  • Feature selection algorithms
  • How to assess the accuracy, performance, completeness, timeliness, stability, interoperability, reliability, and other dynamic aspects of a network anomaly detection system
  • Practical tools for launching attacks, capturing packet or flow traffic, extracting features, detecting attacks, and evaluating detection performance
  • Important unresolved issues and research challenges that need to be overcome to provide better protection for networks

Examining numerous attacks in detail, the authors look at the tools that intruders use and show how to use this knowledge to protect networks. The book also provides material for hands-on development, so that you can code on a testbed to implement detection methods toward the development of your own intrusion detection system. It offers a thorough introduction to the state of the art in network anomaly detection using machine learning approaches and systems.

商品描述(中文翻譯)

隨著互聯網技術的普及和複雜性的迅速提升,以及網絡攻擊數量的增長,網絡入侵檢測變得越來越重要。基於異常的網絡入侵檢測是指與正常行為相比,找出網絡流量數據中的異常或不符合標準的模式。在領域中,發現這些異常模式在網絡安全、信用卡和保險欺詐檢測以及軍事監視敵方活動等方面具有廣泛的應用。《網絡異常檢測:機器學習的視角》深入介紹機器學習技術,以幫助您更有效地檢測和對抗網絡入侵。

在本書中,您將學習以下內容:
- 不同層次的網絡異常和漏洞
- 不同機器學習技術和算法的優缺點
- 基於攻擊特徵和行為特點的攻擊分類
- 特徵選擇算法
- 如何評估網絡異常檢測系統的準確性、性能、完整性、及時性、穩定性、互操作性、可靠性和其他動態方面
- 發動攻擊、捕獲數據包或流量、提取特徵、檢測攻擊和評估檢測性能的實用工具
- 需要解決的重要未解決問題和研究挑戰,以提供更好的網絡保護

通過詳細研究多種攻擊,作者們探討了入侵者使用的工具,並展示如何利用這些知識來保護網絡。本書還提供了實踐開發的材料,讓您可以在測試平台上編寫代碼,實施檢測方法,開發自己的入侵檢測系統。它全面介紹了使用機器學習方法和系統進行網絡異常檢測的最新技術。