Introduction to Machine Learning with Applications in Information Security
暫譯: 機器學習導論:在資訊安全中的應用
Stamp, Mark
- 出版商: CRC
- 出版日期: 2024-12-19
- 售價: $2,330
- 貴賓價: 9.5 折 $2,214
- 語言: 英文
- 頁數: 534
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1032207175
- ISBN-13: 9781032207179
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相關分類:
Machine Learning、資訊安全
海外代購書籍(需單獨結帳)
商品描述
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/.
商品描述(中文翻譯)
《機器學習導論:資訊安全應用(第二版)》提供了一個經過課堂測試的機器學習和深度學習算法及技術的廣泛介紹,並通過現實應用來加強理解。這本書易於理解,不會證明定理或深入數學理論。目標是以直觀的方式呈現主題,並提供足夠的細節以澄清基本概念。
本書深入探討了核心的經典機器學習主題,包括隱馬可夫模型(Hidden Markov Models, HMM)、支持向量機(Support Vector Machines, SVM)和聚類。其他機器學習主題包括k-最近鄰(k-Nearest Neighbor, k-NN)、提升(boosting)、隨機森林(Random Forests)和線性判別分析(Linear Discriminant Analysis, LDA)。基本的深度學習主題包括反向傳播(backpropagation)、卷積神經網絡(Convolutional Neural Networks, CNN)、多層感知器(Multilayer Perceptrons, MLP)和遞迴神經網絡(Recurrent Neural Networks, RNN),這些主題都得到了深入的探討。此外,還介紹了一系列先進的深度學習架構,包括長短期記憶(Long Short-Term Memory, LSTM)、生成對抗網絡(Generative Adversarial Networks, GAN)、極限學習機(Extreme Learning Machines, ELM)、殘差網絡(Residual Networks, ResNet)、深度信念網絡(Deep Belief Networks, DBN)、雙向編碼器表示(Bidirectional Encoder Representations from Transformers, BERT)和Word2Vec。最後,還討論了幾個前沿的深度學習主題,包括隨機失活正則化(dropout regularization)、注意力機制(attention)、可解釋性(explainability)和對抗攻擊(adversarial attacks)。
書中的大多數例子來自資訊安全領域,許多機器學習和深度學習的應用集中於惡意軟體。所呈現的應用通過在簡單的情境中說明各種學習技術的使用,來揭開這些主題的神秘面紗。本書中的一些練習需要編程,並且在某些應用部分假設了基本的計算概念。然而,任何具有適度計算經驗的人都應該能夠輕鬆應對這部分內容。
教學資源,包括PowerPoint簡報、講座視頻和其他相關材料,將在附帶網站上提供:http://www.cs.sjsu.edu/stamp/ML/。
作者簡介
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.
作者簡介(中文翻譯)
馬克·斯坦普(Mark Stamp)是聖荷西州立大學的教授,並且是兩本教科書的作者,分別是《資訊安全:原則與實務》(Information Security: Principles and Practice)和《應用密碼分析:破解現實世界中的密碼》(Applied Cryptanalysis: Breaking Ciphers in the Real World)。他曾在國家安全局(NSA)工作七年,之後又在一家小型矽谷創業公司工作了兩年。