机读格式显示(MARC)
- 000 03076pam a2200325 i 4500
- 008 220216s2023 flua b 001 0 eng
- 020 __ |a 9781032204925 |q hbk
- 020 __ |a 9781032207179 |q pbk
- 020 __ |z 9781003264873 |q ebk
- 040 __ |a DLC |b eng |e rda |c DLC |d ZSU
- 050 00 |a TK5105.59 |b .S735 2022
- 082 00 |a 005.8 |2 23/eng/20220223
- 100 1_ |a Stamp, Mark, |e author.
- 245 10 |a Introduction to machine learning with applications in information security / |c Mark Stamp.
- 250 __ |a Second edition.
- 260 __ |a Boca Raton : |b CRC Press, |c 2023.
- 300 __ |a xxii, 475 pages : |b illustrations ; |c 24 cm.
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 490 0_ |a Chapman & Hall/CRC machine learning & pattern recognition
- 504 __ |a Includes bibliographical references (pages 449-464) and index.
- 520 __ |a "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"-- |c Provided by publisher.
- 650 _0 |a Information networks |x Security measures.
- 650 _0 |a Machine learning.