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.
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.
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.
Inhaltsangabe
1. Preface About the Author 1. What is Machine Learning? 2. A Revealing Introduction to Hidden Markov Models 3. Principles of Principal Component Analysis 4. A Reassuring Introduction to Support Vector Machines 5. A Comprehensible Collection of Clustering Concepts 6. Many Mini Topics 7. Deep Thoughts on Deep Learning 8. Onward to Backpropagation 9. A Deeper Diver into Deep Learning 10. Alphabet Soup of Deep Learning Topics 11. HMMs for Classic Cryptanalysis 12. Image Spam Detection 13. Image-Based Malware Analysis 14. Malware Evolution Detection 15. Experimental Design and Analysis 16. Epilogue References Index
1. Preface About the Author 1. What is Machine Learning? 2. A Revealing Introduction to Hidden Markov Models 3. Principles of Principal Component Analysis 4. A Reassuring Introduction to Support Vector Machines 5. A Comprehensible Collection of Clustering Concepts 6. Many Mini Topics 7. Deep Thoughts on Deep Learning 8. Onward to Backpropagation 9. A Deeper Diver into Deep Learning 10. Alphabet Soup of Deep Learning Topics 11. HMMs for Classic Cryptanalysis 12. Image Spam Detection 13. Image-Based Malware Analysis 14. Malware Evolution Detection 15. Experimental Design and Analysis 16. Epilogue References Index
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