000			03238cam a22003858i 4500
001			22425912
003			JO-AjAnu
005			20241020101451.0
008			220216s2023 flu b 001 0 eng
010			_a 2022007073
020			_a9781032204925 _q(hbk)
020			_a9781032207179 _q(pbk)
020			_z9781003264873 _q(ebk)
040			_aDLC _beng _erda _cDLC _dJO-AjAnu
042			_apcc
050	0	0	_aTK5105.59 _b.S735 2023
082	0	0	_a005.8 _223/eng/20220223
100	1		_aStamp, Mark, _eauthor.
245	1	0	_aIntroduction to machine learning with applications in information security / _cMark Stamp.
250			_aSecond edition.
263			_a2208
264		1	_aBoca Raton : _bCRC Press, _c[2023]
300			_apages cm.
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
490	0		_aChapman & Hall/CRC machine learning & pattern recognition
504			_aIncludes 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"-- _cProvided by publisher.
650		0	_aInformation networks _xSecurity measures.
650		0	_aMachine learning.
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c33794 _d33794