Data Mining and Machine Learning in Cybersecurity

Book 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

Table of contents

  1. Front Cover
  2. Contents (1/2)
  3. Contents (2/2)
  4. List of Figures
  5. List of Tables
  6. Preface
  7. Authors
  8. Chapter 1: Introduction (1/5)
  9. Chapter 1: Introduction (2/5)
  10. Chapter 1: Introduction (3/5)
  11. Chapter 1: Introduction (4/5)
  12. Chapter 1: Introduction (5/5)
  13. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (1/7)
  14. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (2/7)
  15. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (3/7)
  16. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (4/7)
  17. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (5/7)
  18. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (6/7)
  19. Chapter 2: Classical Machine-Learning Paradigms for Data Mining (7/7)
  20. Chapter 3: Supervised Learning for Misuse/Signature Detection (1/6)
  21. Chapter 3: Supervised Learning for Misuse/Signature Detection (2/6)
  22. Chapter 3: Supervised Learning for Misuse/Signature Detection (3/6)
  23. Chapter 3: Supervised Learning for Misuse/Signature Detection (4/6)
  24. Chapter 3: Supervised Learning for Misuse/Signature Detection (5/6)
  25. Chapter 3: Supervised Learning for Misuse/Signature Detection (6/6)
  26. Chapter 4: Machine Learning for Anomaly Detection (1/6)
  27. Chapter 4: Machine Learning for Anomaly Detection (2/6)
  28. Chapter 4: Machine Learning for Anomaly Detection (3/6)
  29. Chapter 4: Machine Learning for Anomaly Detection (4/6)
  30. Chapter 4: Machine Learning for Anomaly Detection (5/6)
  31. Chapter 4: Machine Learning for Anomaly Detection (6/6)
  32. Chapter 5: Machine Learning for Hybrid Detection (1/5)
  33. Chapter 5: Machine Learning for Hybrid Detection (2/5)
  34. Chapter 5: Machine Learning for Hybrid Detection (3/5)
  35. Chapter 5: Machine Learning for Hybrid Detection (4/5)
  36. Chapter 5: Machine Learning for Hybrid Detection (5/5)
  37. Chapter 6: Machine Learning for Scan Detection (1/4)
  38. Chapter 6: Machine Learning for Scan Detection (2/4)
  39. Chapter 6: Machine Learning for Scan Detection (3/4)
  40. Chapter 6: Machine Learning for Scan Detection (4/4)
  41. Chapter 7: Machine Learning for Profiling Network Traffic (1/4)
  42. Chapter 7: Machine Learning for Profiling Network Traffic (2/4)
  43. Chapter 7: Machine Learning for Profiling Network Traffic (3/4)
  44. Chapter 7: Machine Learning for Profiling Network Traffic (4/4)
  45. Chapter 8: Privacy-Preserving Data Mining (1/6)
  46. Chapter 8: Privacy-Preserving Data Mining (2/6)
  47. Chapter 8: Privacy-Preserving Data Mining (3/6)
  48. Chapter 8: Privacy-Preserving Data Mining (4/6)
  49. Chapter 8: Privacy-Preserving Data Mining (5/6)
  50. Chapter 8: Privacy-Preserving Data Mining (6/6)
  51. Chapter 9: Emerging Challenges in Cybersecurity (1/4)
  52. Chapter 9: Emerging Challenges in Cybersecurity (2/4)
  53. Chapter 9: Emerging Challenges in Cybersecurity (3/4)
  54. Chapter 9: Emerging Challenges in Cybersecurity (4/4)
  55. Back Cover

Product information

  • Title: Data Mining and Machine Learning in Cybersecurity
  • Author(s): Sumeet Dua, Xian Du
  • Release date: April 2016
  • Publisher(s): Auerbach Publications
  • ISBN: 9781439839430