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/.

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/.

Provides systematic guidance on meeting the information security challenges of the 21st century, featuring newly revised material throughout

Information Security: Principles and Practice is the must-have book for students, instructors, and early-stage professionals alike. Author Mark Stamp provides clear, accessible, and accurate information on the four critical components of information security: cryptography, access control, security protocols, and software. Readers are provided with a wealth of real-world examples that clarify complex topics, highlight important security issues, and demonstrate effective methods and strategies for protecting the confidentiality and integrity of data.

Fully revised and updated, the third edition of Information Security features a brand-new chapter on network security basics and expanded coverage of cross-site scripting (XSS) attacks, Stuxnet and other malware, the SSH protocol, secure software development, and security protocols. Fresh examples illustrate the Rivest-Shamir-Adleman (RSA) cryptosystem, Elliptic-curve cryptography (ECC), and hash functions based on bitcoin and blockchains. Updated problem sets, figures, tables, and graphs help readers develop a working knowledge of classic cryptosystems, symmetric and public key cryptography, cryptanalysis, simple authentication protocols, intrusion and malware detection systems, and more. Presenting a highly practical approach to information security, this popular textbook:

Information Security: Principles and Practice, Third Edition is the perfect textbook for advanced undergraduate and graduate students in all Computer Science programs, and remains essential reading for professionals working in industrial or government security.To request supplementary materials, please contact mark.stamp@sjsu.edu and visit the author-maintained website for more: https://www.cs.sjsu.edu/~stamp/infosec/.

Provides systematic guidance on meeting the information security challenges of the 21st century, featuring newly revised material throughout

Information Security: Principles and Practice is the must-have book for students, instructors, and early-stage professionals alike. Author Mark Stamp provides clear, accessible, and accurate information on the four critical components of information security: cryptography, access control, security protocols, and software. Readers are provided with a wealth of real-world examples that clarify complex topics, highlight important security issues, and demonstrate effective methods and strategies for protecting the confidentiality and integrity of data.

Fully revised and updated, the third edition of Information Security features a brand-new chapter on network security basics and expanded coverage of cross-site scripting (XSS) attacks, Stuxnet and other malware, the SSH protocol, secure software development, and security protocols. Fresh examples illustrate the Rivest-Shamir-Adleman (RSA) cryptosystem, Elliptic-curve cryptography (ECC), and hash functions based on bitcoin and blockchains. Updated problem sets, figures, tables, and graphs help readers develop a working knowledge of classic cryptosystems, symmetric and public key cryptography, cryptanalysis, simple authentication protocols, intrusion and malware detection systems, and more. Presenting a highly practical approach to information security, this popular textbook:

Information Security: Principles and Practice, Third Edition is the perfect textbook for advanced undergraduate and graduate students in all Computer Science programs, and remains essential reading for professionals working in industrial or government security.To request supplementary materials, please contact mailto:mark.stamp@sjsu.edu and visit the author-maintained website for more https://www.cs.sjsu.edu/~stamp/infosec/.

This book is a succinct guide to company law. The reader is guided through the elements involved in forming a company, and other vital areas are explained in detail, including: the availability of public information on companies and how to find it; directors'' obligations; minority shareholders'' rights; the memorandum and articles of association; how a company should execute a document; company meetings and charges; and debentures.

This third edition has been updated to include consideration of recent important cases, as well as key statutory instruments that have impacted upon company law since the last edition. It also includes a section on dividends and an analysis of the DTIs proposals for reform of company charges.

This book is a succinct guide to company law. The reader is guided through the elements involved in forming a company, and other vital areas are explained in detail, including: the availability of public information on companies and how to find it; directors'' obligations; minority shareholders'' rights; the memorandum and articles of association; how a company should execute a document; company meetings and charges; and debentures.

This third edition has been updated to include consideration of recent important cases, as well as key statutory instruments that have impacted upon company law since the last edition. It also includes a section on dividends and an analysis of the DTIs proposals for reform of company charges.

​This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed.

This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity.

This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more.

Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

This book addresses a variety of problems that arise at the interface between AI techniques and challenging problems in cybersecurity. The book covers many of the issues that arise when applying AI and deep learning algorithms to inherently difficult problems in the security domain, such as malware detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required.

This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.

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/.

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/.

Provides systematic guidance on meeting the information security challenges of the 21st century, featuring newly revised material throughout

Information Security: Principles and Practice is the must-have book for students, instructors, and early-stage professionals alike. Author Mark Stamp provides clear, accessible, and accurate information on the four critical components of information security: cryptography, access control, security protocols, and software. Readers are provided with a wealth of real-world examples that clarify complex topics, highlight important security issues, and demonstrate effective methods and strategies for protecting the confidentiality and integrity of data.

Fully revised and updated, the third edition of Information Security features a brand-new chapter on network security basics and expanded coverage of cross-site scripting (XSS) attacks, Stuxnet and other malware, the SSH protocol, secure software development, and security protocols. Fresh examples illustrate the Rivest-Shamir-Adleman (RSA) cryptosystem, Elliptic-curve cryptography (ECC), and hash functions based on bitcoin and blockchains. Updated problem sets, figures, tables, and graphs help readers develop a working knowledge of classic cryptosystems, symmetric and public key cryptography, cryptanalysis, simple authentication protocols, intrusion and malware detection systems, and more. Presenting a highly practical approach to information security, this popular textbook:

Information Security: Principles and Practice, Third Edition is the perfect textbook for advanced undergraduate and graduate students in all Computer Science programs, and remains essential reading for professionals working in industrial or government security.To request supplementary materials, please contact mark.stamp@sjsu.edu and visit the author-maintained website for more: https://www.cs.sjsu.edu/~stamp/infosec/.

Provides systematic guidance on meeting the information security challenges of the 21st century, featuring newly revised material throughout

Information Security: Principles and Practice is the must-have book for students, instructors, and early-stage professionals alike. Author Mark Stamp provides clear, accessible, and accurate information on the four critical components of information security: cryptography, access control, security protocols, and software. Readers are provided with a wealth of real-world examples that clarify complex topics, highlight important security issues, and demonstrate effective methods and strategies for protecting the confidentiality and integrity of data.

Fully revised and updated, the third edition of Information Security features a brand-new chapter on network security basics and expanded coverage of cross-site scripting (XSS) attacks, Stuxnet and other malware, the SSH protocol, secure software development, and security protocols. Fresh examples illustrate the Rivest-Shamir-Adleman (RSA) cryptosystem, Elliptic-curve cryptography (ECC), and hash functions based on bitcoin and blockchains. Updated problem sets, figures, tables, and graphs help readers develop a working knowledge of classic cryptosystems, symmetric and public key cryptography, cryptanalysis, simple authentication protocols, intrusion and malware detection systems, and more. Presenting a highly practical approach to information security, this popular textbook:

Information Security: Principles and Practice, Third Edition is the perfect textbook for advanced undergraduate and graduate students in all Computer Science programs, and remains essential reading for professionals working in industrial or government security.To request supplementary materials, please contact mailto:mark.stamp@sjsu.edu and visit the author-maintained website for more https://www.cs.sjsu.edu/~stamp/infosec/.

This book is a succinct guide to company law. The reader is guided through the elements involved in forming a company, and other vital areas are explained in detail, including: the availability of public information on companies and how to find it; directors'' obligations; minority shareholders'' rights; the memorandum and articles of association; how a company should execute a document; company meetings and charges; and debentures.

This third edition has been updated to include consideration of recent important cases, as well as key statutory instruments that have impacted upon company law since the last edition. It also includes a section on dividends and an analysis of the DTIs proposals for reform of company charges.

This book is a succinct guide to company law. The reader is guided through the elements involved in forming a company, and other vital areas are explained in detail, including: the availability of public information on companies and how to find it; directors'' obligations; minority shareholders'' rights; the memorandum and articles of association; how a company should execute a document; company meetings and charges; and debentures.

This third edition has been updated to include consideration of recent important cases, as well as key statutory instruments that have impacted upon company law since the last edition. It also includes a section on dividends and an analysis of the DTIs proposals for reform of company charges.

​This book is focused on the use of deep learning (DL) and artificial intelligence (AI) as tools to advance the fields of malware detection and analysis. The individual chapters of the book deal with a wide variety of state-of-the-art AI and DL techniques, which are applied to a number of challenging malware-related problems. DL and AI based approaches to malware detection and analysis are largely data driven and hence minimal expert domain knowledge of malware is needed.

This book explores new and novel applications of machine learning, deep learning, and artificial intelligence that are related to major challenges in the field of cybersecurity. The provided research goes beyond simply applying AI techniques to datasets and instead delves into deeper issues that arise at the interface between deep learning and cybersecurity.

This book also provides insight into the difficult "how" and "why" questions that arise in AI within the security domain. For example, this book includes chapters covering "explainable AI", "adversarial learning", "resilient AI", and a wide variety of related topics. It’s not limited to any specific cybersecurity subtopics and the chapters touch upon a wide range of cybersecurity domains, ranging from malware to biometrics and more.

Researchers and advanced level students working and studying in the fields of cybersecurity (equivalently, information security) or artificial intelligence (including deep learning, machine learning, big data, and related fields) will want to purchase this book as a reference. Practitioners working within these fields will also be interested in purchasing this book.

This book addresses a variety of problems that arise at the interface between AI techniques and challenging problems in cybersecurity. The book covers many of the issues that arise when applying AI and deep learning algorithms to inherently difficult problems in the security domain, such as malware detection and analysis, intrusion detection, spam detection, and various other subfields of cybersecurity. The book places particular attention on data driven approaches, where minimal expert domain knowledge is required.

This book bridges some of the gaps that exist between deep learning/AI research and practical problems in cybersecurity. The proposed topics cover a wide range of deep learning and AI techniques, including novel frameworks and development tools enabling the audience to innovate with these cutting-edge research advancements in various security-related use cases. The book is timely since it is not common to find clearly elucidated research that applies the latest developments in AI to problems in cybersecurity.