Data Mining and Machine Learning in Cybersecurity Front Cover

Data Mining and Machine Learning in Cybersecurity

  • Length: 256 pages
  • Edition: 1
  • Publisher:
  • Publication Date: 2011-04-25
  • ISBN-10: 1439839425
  • ISBN-13: 9781439839423
  • Sales Rank: #1954471 (See Top 100 Books)
Description

With the rapid advancement of information discovery techniques, machine learning and data mining continue to play a significant role in cybersecurity. Although several conferences, workshops, and journals focus on the fragmented research topics in this area, there has been no single interdisciplinary resource on past and current works and possible paths for future research in this area. This book fills this need.

From basic concepts in machine learning and data mining to advanced problems in the machine learning domain, Data Mining and Machine Learning in Cybersecurity provides a unified reference for specific machine learning solutions to cybersecurity problems. It supplies a foundation in cybersecurity fundamentals and surveys contemporary challenges—detailing cutting-edge machine learning and data mining techniques. It also:

  • Unveils cutting-edge techniques for detecting new attacks
  • Contains in-depth discussions of machine learning solutions to detection problems
  • Categorizes methods for detecting, scanning, and profiling intrusions and anomalies
  • Surveys contemporary cybersecurity problems and unveils state-of-the-art machine learning and data mining solutions
  • Details privacy-preserving data mining methods

This interdisciplinary resource includes technique review tables that allow for speedy access to common cybersecurity problems and associated data mining methods. Numerous illustrative figures help readers visualize the workflow of complex techniques and more than forty case studies provide a clear understanding of the design and application of data mining and machine learning techniques in cybersecurity.

Table of Contents

Chapter 1: Introduction
Chapter 2: Classical Machine-Learning Paradigms for Data Mining
Chapter 3: Supervised Learning for Misuse/Signature Detection
Chapter 4: Machine Learning for Anomaly Detection
Chapter 5: Machine Learning for Hybrid Detection
Chapter 6: Machine Learning for Scan Detection
Chapter 7: Machine Learning for Profiling Network Traffic
Chapter 8: Privacy-Preserving Data Mining
Chapter 9: Emerging Challenges in Cybersecurity

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