Practical AI for Cybersecurity (Record no. 17682)

MARC details
000 -LEADER
fixed length control field 06658cam a2200577Mu 4500
001 - CONTROL NUMBER
control field 9781003005230
003 - CONTROL NUMBER IDENTIFIER
control field FlBoTFG
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220724194438.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m o d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cnu---unuuu
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 201212s2021 xx o ||| 0 eng d
040 ## - CATALOGING SOURCE
Original cataloging agency OCoLC-P
Language of cataloging eng
Transcribing agency OCoLC-P
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000349450
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000349454
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781003005230
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1003005233
Qualifying information (electronic bk.)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000349443
Qualifying information (electronic bk. : Mobipocket)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000349446
Qualifying information (electronic bk. : Mobipocket)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781000349436
Qualifying information (electronic bk. : PDF)
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 1000349438
Qualifying information (electronic bk. : PDF)
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1226581075
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC-P)1226581075
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.A25
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 051240
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 053000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code COM
Subject category code subdivision 004000
Source bisacsh
072 #7 - SUBJECT CATEGORY CODE
Subject category code UR
Source bicssc
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 005.8
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Das, Ravindra.
245 10 - TITLE STATEMENT
Title Practical AI for Cybersecurity
Medium [electronic resource].
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Milton :
Name of publisher, distributor, etc. Auerbach Publishers, Incorporated,
Date of publication, distribution, etc. 2021.
300 ## - PHYSICAL DESCRIPTION
Extent 1 online resource (0 p.)
500 ## - GENERAL NOTE
General note Description based upon print version of record.
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note Intro -- Half Title -- Title Page -- Copyright Page -- Dedication -- Table of Contents -- Acknowledgments -- Notes on Contributors -- Chapter 1 Artificial Intelligence -- The Chronological Evolution of Cybersecurity -- An Introduction to Artificial Intelligence -- The Sub-Fields of Artificial Intelligence -- Machine Learning -- Neural Networks -- Computer Vision -- A Brief Overview of This Book -- The History of Artificial Intelligence -- The Origin Story -- The Golden Age for Artificial Intelligence -- The Evolution of Expert Systems -- The Importance of Data in Artificial Intelligence
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note The Fundamentals of Data Basics -- The Types of Data that are Available -- Big Data -- Understanding Preparation of Data -- Other Relevant Data Concepts that are Important to Artificial Intelligence -- Resources -- Chapter 2 Machine Learning -- The High Level Overview -- The Machine Learning Process -- Data Order -- Picking the Algorithm -- Training the Model -- Model Evaluation -- Fine Tune the Model -- The Machine Learning Algorithm Classifications -- The Machine Learning Algorithms -- Key Statistical Concepts -- The Deep Dive into the Theoretical Aspects of Machine Learning
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Understanding Probability -- The Bayesian Theorem -- The Probability Distributions for Machine Learning -- The Normal Distribution -- Supervised Learning -- The Decision Tree -- The Problem of Overfitting the Decision Tree -- The Random Forest -- Bagging -- The Naïve Bayes Method -- The KNN Algorithm -- Unsupervised Learning -- Generative Models -- Data Compression -- Association -- The Density Estimation -- The Kernel Density Function -- Latent Variables -- Gaussian Mixture Models -- The Perceptron -- Training a Perceptron -- The Boolean Functions -- The Multiple Layer Perceptrons
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note The Multi-Layer Perceptron (MLP): A Statistical Approximator -- The Backpropagation Algorithm -- The Nonlinear Regression -- The Statistical Class Descriptions in Machine Learning -- Two Class Statistical Discrimination -- Multiclass Distribution -- Multilabel Discrimination -- Overtraining -- How a Machine Learning System can Train from Hidden, Statistical Representation -- Autoencoders -- The Word2vec Architecture -- Application of Machine Learning to Endpoint Protection -- Feature Selection and Feature Engineering for Detecting Malware -- Common Vulnerabilities and Exposures (CVE)
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note Text Strings -- Byte Sequences -- Opcodes -- API, System Calls, and DLLs -- Entropy -- Feature Selection Process for Malware Detection -- Feature Selection Process for Malware Classification -- Training Data -- Tuning of Malware Classification Models Using a Receiver Operating Characteristic Curve -- Detecting Malware after Detonation -- Summary -- Applications of Machine Learning Using Python -- The Use of Python Programming in the Healthcare Sector -- How Machine Learning is Used with a Chatbot -- The Strategic Advantages of Machine Learning In Chatbots
520 ## - SUMMARY, ETC.
Summary, etc. The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note OCLC-licensed vendor bibliographic record.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Artificial intelligence.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer security.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Neural networks (Neurobiology)
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Programming / Systems Analysis & Design
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Security / General
Source of heading or term bisacsh
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element COMPUTERS / Artificial Intelligence
Source of heading or term bisacsh
856 40 - ELECTRONIC LOCATION AND ACCESS
Materials specified Read Online
Uniform Resource Identifier <a href="https://www.taylorfrancis.com/books/9781003005230">https://www.taylorfrancis.com/books/9781003005230</a>
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified OCLC metadata license agreement
Uniform Resource Identifier <a href="http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</a>
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type eBook

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