Introduction to Machine Learning with Applications in Information Security

Regular price €55.99
A01=Mark Stamp
Age Group_Uncategorized
Age Group_Uncategorized
Anti-virus Software
Antivirus Software
Author_Mark Stamp
automatic-update
Category1=Non-Fiction
Category=KCH
Category=KCHS
Category=UMK
Category=UR
Category=UT
Category=UYA
Category=UYQM
Cluster Random Sampling
computer and network security
Convolutional Layer
COP=United Kingdom
cryptography
Delivery_Pre-order
Dl Model
Elementary Sampling Units
Em Algorithm
Em Cluster
eq_business-finance-law
eq_computing
eq_isMigrated=2
eq_new_release
eq_non-fiction
Homophonic Substitution
intrusion detection
Language_English
Linear SVM
malware detection
Malware Families
Malware Samples
Mode Automatic Differentiation
PA=Not yet available
pattern recognition
PR Curve
Price_€50 to €100
PS=Forthcoming
Putative Key
Random Restarts
Regular Grid Sampling
RNN Architecture
Roc Curve
Single Layer Perceptron
SMO Algorithm
softlaunch
statistical learning
SVM Model
SVM Training
SVM Weight

Product details

  • ISBN 9781032207179
  • Weight: 453g
  • Dimensions: 156 x 234mm
  • Publication Date: 19 Dec 2024
  • Publisher: Taylor & Francis Ltd
  • Publication City/Country: GB
  • Product Form: Paperback
  • Language: English
Delivery/Collection within 10-20 working days

Our Delivery Time Frames Explained
2-4 Working Days: Available in-stock

10-20 Working Days
: On Backorder

Will Deliver When Available
: On Pre-Order or Reprinting

We ship your order once all items have arrived at our warehouse and are processed. Need those 2-4 day shipping items sooner? Just place a separate order for them!

Introduction to Machine Learning with Applications in Information Security, Second Edition provides a classroom-tested introduction to a wide variety of machine learning and deep learning algorithms and techniques, reinforced via realistic applications. The book is accessible and doesn’t prove theorems, or dwell on mathematical theory. The goal is to present topics at an intuitive level, with just enough detail to clarify the underlying concepts.

The book covers core classic machine learning topics in depth, including Hidden Markov Models (HMM), Support Vector Machines (SVM), and clustering. Additional machine learning topics include k-Nearest Neighbor (k-NN), boosting, Random Forests, and Linear Discriminant Analysis (LDA). The fundamental deep learning topics of backpropagation, Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), and Recurrent Neural Networks (RNN) are covered in depth. A broad range of advanced deep learning architectures are also presented, including Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), Extreme Learning Machines (ELM), Residual Networks (ResNet), Deep Belief Networks (DBN), Bidirectional Encoder Representations from Transformers (BERT), and Word2Vec. Finally, several cutting-edge deep learning topics are discussed, including dropout regularization, attention, explainability, and adversarial attacks.

Most of the examples in the book are drawn from the field of information security, with many of the machine learning and deep learning applications focused on malware. The applications presented serve to demystify the topics by illustrating the use of various learning techniques in straightforward scenarios. Some of the exercises in this book require programming, and elementary computing concepts are assumed in a few of the application sections. However, anyone with a modest amount of computing experience should have no trouble with this aspect of the book.

Instructor resources, including PowerPoint slides, lecture videos, and other relevant material are provided on an accompanying website: http://www.cs.sjsu.edu/~stamp/ML/.

Mark Stamp is a Professor at San Jose State University, and the author of two textbooks, Information Security: Principles and Practice and Applied Cryptanalysis: Breaking Ciphers in the Real World. He previously worked at the National Security Agency (NSA) for seven years, which was followed by two years at a small Silicon Valley startup company.