Cybersecurity Analytics

Verma, Rakesh M., Marchette, David J.

  • 出版商: CRC
  • 出版日期: 2019-11-20
  • 售價: $3,420
  • 貴賓價: 9.5$3,249
  • 語言: 英文
  • 頁數: 356
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 036734601X
  • ISBN-13: 9780367346010
  • 相關分類: 資訊安全
  • 下單後立即進貨 (約2~4週)



Cybersecurity Analytics is for the cybersecurity student and professional who wants to learn data science techniques critical for tackling cybersecurity challenges, and for the data science student and professional who wants to learn about cybersecurity adaptations. Trying to build a malware detector, a phishing email detector, or just interested in finding patterns in your datasets? This book can let you do it on your own. Numerous examples and datasets links are included so that the reader can "learn by doing." Anyone with a basic college-level calculus course and some probability knowledge can easily understand most of the material.

The book includes chapters containing: unsupervised learning, semi-supervised learning, supervised learning, text mining, natural language processing, and more. It also includes background on security, statistics, and linear algebra. The website for the book contains a listing of datasets, updates, and other resources for serious practitioners.


Rakesh Verma is a professor of computer science at the University of Houston where he is leading a research group that applies reasoning and data science to cybersecurity challenges. He teaches a course on security analytics that includes some of the material here. Since 2015, he has been co-organizing and editing the proceedings of the ACM International Workshop on Security and Privacy Analytics. He is an editor of Frontiers of Big Data in the Cybersecurity Area, an ACM Distinguished Speaker (2011-2018), and the winner of two Best Paper Awards. He received the Lifetime Mentoring Award from the University of Houston and he is a Fulbright Senior Specialist in Computer Science.

David Marchette is a principal scientist at the Naval Surface Warfare Center, Dahlgren Division where he is responsible for leading basic and applied research projects in computational statistics, graph theory, network analysis, pattern recognition, computer intrusion detection, and text analysis. He is a fellow of the American Statistical Association (ASA) and the American Association for the Advancement of Science (AAAS) and an elected member of the International Statistical Institute (ISI).