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Introduction to machine learning with applications in information security

Introduction to machine learning with applications in information security

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서명 / 저자사항
Introduction to machine learning with applications in information security / Mark Stamp, San Jose State University, California.
발행사항
Boca Raton : CRC Press, Taylor & Francis Group, c2018.
형태사항
xiv, 345 p. ; 25 cm.
총서사항
Chapman & Hall/CRC machine learning & pattern recognition ;16
ISBN
9781138626782 (hardback : acid-free paper)
서지주기
Includes bibliographical references (p. 319-337) and index.
일반주제명
Information networks --Security measures. Machine learning.
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020 ▼a 9781138626782 (hardback : acid-free paper)
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040 ▼a DLC ▼b eng ▼c DLC ▼e rda ▼d DLC ▼d 211009
050 0 0 ▼a TK5105.59 ▼b .S735 2018
082 0 0 ▼a 004.6 ▼2 23
084 ▼a 004.6 ▼2 DDCK
090 ▼a 004.6 ▼b S7832i
100 1 ▼a Stamp, Mark.
245 1 0 ▼a Introduction to machine learning with applications in information security / ▼c Mark Stamp, San Jose State University, California.
260 ▼a Boca Raton : ▼b CRC Press, Taylor & Francis Group, ▼c c2018.
300 ▼a xiv, 345 p. ; ▼c 25 cm.
490 1 ▼a Chapman & Hall/CRC machine learning & pattern recognition ; ▼v 16
504 ▼a Includes bibliographical references (p. 319-337) and index.
650 0 ▼a Information networks ▼x Security measures.
650 0 ▼a Machine learning.
830 0 ▼a Chapman & Hall/CRC machine learning & pattern recognition ; ▼v 16.
945 ▼a KLPA

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 과학도서관/단행본실(2층)/ 청구기호 004.6 S7832i 등록번호 121245134 도서상태 대출가능 반납예정일 예약 서비스 B M

컨텐츠정보

저자소개

마크 스탬프(지은이)

Texas Tech University에서 수학 석사 및 박사학위를 취득하였다. 현재 실리콘밸리에 있는 산호세 주립대 전산학과 교수로 정보보안을 강의하고 있다. 정보보안 관련 기업과 학계에서 풍부한 실무 경험을 쌓았으며, 미 국가보안국(NSA)에서 암호분석가로 7년간 근무하였다. stamp@cs.sjsu.edu

정보제공 : Aladin

목차

Introduction

What is Machine Learning? ?

About This Book?

Necessary Background

A Few Too Many Notes

I TOOLS OF THE TRADE

A Revealing Introduction to Hidden Markov Models

Introduction and Background

A Simple Example

Notation

The Three Problems

The Three Solutions

Dynamic Programming ?

Scaling?

All Together Now

The Bottom Line?

A Full Frontal View of Profile Hidden Markov Models?

Introduction

Overview and Notation

Pairwise Alignment

Multiple Sequence Alignment

PHMM from MSA

Scoring

The Bottom Line

Principal Components of Principal Component Analysis

Introduction?

Background

Principal Component Analysis ?

SVD Basics ?

All Together Now

A Numerical Example ?

The Bottom Line?

A Reassuring Introduction to Support Vector Machines

Introduction?

Constrained Optimization

AC loser Look at SVM

All Together Now?

A Note on Quadratic Programming?

The Bottom Line?

Problems ?

A Comprehensible Collection of Clustering Concepts

Introduction

Overview and Background

-Means

Measuring Cluster Quality

EM Clustering

The Bottom Line

Problems

Many Mini Topics

Introduction

-Nearest Neighbors

Neural Networks

Boosting

Random Forest

Linear Discriminant Analysis

VectorQuantization

Naive Bayes

Regression Analysis

Conditional Random Fields

Data Analysis

Introduction

Experimental Design

Accuracy

ROC Curves

Imbalance Problem

PR Curves

The Bottom Line

II APPLICATIONS

HMM Applications

Introduction

English Text Analysis ?

Detecting "Undetectable" Malware?

Classic Cryptanalysis

PHMM Applications

Introduction

Masquerade Detection

Malware Detection

PCA Applications

Introduction

Eigenfaces

Eigenviruses

Eigenspam

SVM Applications

Introduction

Malware Detection

Image Spam Revisited

Clustering Applications

Introduction

-Means for Malware Classification

EM vs -Means for Malware Analysis


정보제공 : Aladin

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