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Introduction to machine learning with applications in information security

Record Type:	Electronic resources : Monograph/item
Title/Author:	Introduction to machine learning with applications in information securityMark Stamp.
Author:	Stamp, Mark.
Published:	Boca Raton, FL :CRC Press,2023.
Description:	1 online resource.
Subject:	Information networksSecurity measures.
Online resource:	https://www.taylorfrancis.com/books/9781003264873
ISBN:	9781003264873$q(electronic bk.)

Introduction to machine learning with applications in information security
Stamp, Mark.

Introduction to machine learning with applications in information security[electronic resource] /Mark Stamp. - 2nd ed. - Boca Raton, FL :CRC Press,2023. - 1 online resource. - Chapman & Hall/CRC machine learning & pattern recognition. - Chapman & Hall/CRC machine learning & pattern recognition..

Includes bibliographical references and index.

"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"--

ISBN: 9781003264873$q(electronic bk.)

LCCN: 2022007073Subjects--Topical Terms:

283209
Information networks
--Security measures.

LC Class. No.: TK5105.59 / .S735 2022

Dewey Class. No.: 005.8

Introduction to machine learning with applications in information security
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245 1 0 $a Introduction to machine learning with applications in information security $h [electronic resource] / $c Mark Stamp.
250 $a 2nd ed.
260 $a Boca Raton, FL : $b CRC Press, $c 2023.
300 $a 1 online resource.
490 1 $a Chapman & Hall/CRC machine learning & pattern recognition
504 $a Includes bibliographical references 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.
588 $a Description based on print version record.
650 0 $a Information networks $x Security measures. $3 283209
650 0 $a Machine learning. $3 188639
830 0 $a Chapman & Hall/CRC machine learning & pattern recognition. $3 976554
856 4 0 $u https://www.taylorfrancis.com/books/9781003264873