東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Adversary-aware learning techniques ...

Dasgupta, Prithviraj.

FindBook

Google Book

Amazon

博客來

Adversary-aware learning techniques and trends in cybersecurity

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Adversary-aware learning techniques and trends in cybersecurity/ edited by Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu.
其他作者:	Dasgupta, Prithviraj.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	x, 227 p. :ill. (some col.), digital ;24 cm.
內容註:	Part I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses -- 1. Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning -- 2. Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM -- 3. Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games -- Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks -- 4. Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model -- 5. Overview of GANs for Image Synthesis and Detection Methods -- 6. Robust Machine Learning using Diversity and Blockchain -- Part III: Human Machine Interactions and Roles in Automated Cyber Defenses -- 7. Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents -- 8. Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks -- 9. Homology as an Adversarial Attack Indicator -- Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs)
Contained By:	Springer Nature eBook
標題:	Computer security. -
電子資源:	https://doi.org/10.1007/978-3-030-55692-1
ISBN:	9783030556921

Adversary-aware learning techniques and trends in cybersecurity
Adversary-aware learning techniques and trends in cybersecurity[electronic resource] /edited by Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu. - Cham :Springer International Publishing :2021. - x, 227 p. :ill. (some col.), digital ;24 cm.

Part I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses -- 1. Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning -- 2. Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM -- 3. Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games -- Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks -- 4. Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model -- 5. Overview of GANs for Image Synthesis and Detection Methods -- 6. Robust Machine Learning using Diversity and Blockchain -- Part III: Human Machine Interactions and Roles in Automated Cyber Defenses -- 7. Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents -- 8. Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks -- 9. Homology as an Adversarial Attack Indicator -- Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs)

This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.

ISBN: 9783030556921

Standard No.: 10.1007/978-3-030-55692-1doiSubjects--Topical Terms:

540555
Computer security.

LC Class. No.: QA76.9.A25 / A38 2021

Dewey Class. No.: 005.8

Adversary-aware learning techniques and trends in cybersecurity
LDR:03230nmm a2200325 a 4500 001 2237335
003 DE-He213
005 20210122183801.0
006 m d
007 cr nn 008maaau
008 211111s2021 sz s 0 eng d
020 $a 9783030556921 $q (electronic bk.)
020 $a 9783030556914 $q (paper)
024 7 $a 10.1007/978-3-030-55692-1 $2 doi
035 $a 978-3-030-55692-1
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.A25 $b A38 2021
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 005.8 $2 23
090 $a QA76.9.A25 $b A244 2021
245 0 0 $a Adversary-aware learning techniques and trends in cybersecurity $h [electronic resource] / $c edited by Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a x, 227 p. : $b ill. (some col.), digital ; $c 24 cm.
505 0 $a Part I: Game-Playing AI and Game Theory-based Techniques for Cyber Defenses -- 1. Rethinking Intelligent Behavior as Competitive Games for Handling Adversarial Challenges to Machine Learning -- 2. Security of Distributed Machine Learning:A Game-Theoretic Approach to Design Secure DSVM -- 3. Be Careful When Learning Against Adversaries: Imitative Attacker Deception in Stackelberg Security Games -- Part II: Data Modalities and Distributed Architectures for Countering Adversarial Cyber Attacks -- 4. Adversarial Machine Learning in Text: A Case Study of Phishing Email Detection with RCNN model -- 5. Overview of GANs for Image Synthesis and Detection Methods -- 6. Robust Machine Learning using Diversity and Blockchain -- Part III: Human Machine Interactions and Roles in Automated Cyber Defenses -- 7. Automating the Investigation of Sophisticated Cyber Threats with Cognitive Agents -- 8. Integrating Human Reasoning and Machine Learning to Classify Cyber Attacks -- 9. Homology as an Adversarial Attack Indicator -- Cyber-(in)security, revisited: Proactive Cyber-defenses, Interdependence and Autonomous Human Machine Teams (A-HMTs)
520 $a This book is intended to give researchers and practitioners in the cross-cutting fields of artificial intelligence, machine learning (AI/ML) and cyber security up-to-date and in-depth knowledge of recent techniques for improving the vulnerabilities of AI/ML systems against attacks from malicious adversaries. The ten chapters in this book, written by eminent researchers in AI/ML and cyber-security, span diverse, yet inter-related topics including game playing AI and game theory as defenses against attacks on AI/ML systems, methods for effectively addressing vulnerabilities of AI/ML operating in large, distributed environments like Internet of Things (IoT) with diverse data modalities, and, techniques to enable AI/ML systems to intelligently interact with humans that could be malicious adversaries and/or benign teammates. Readers of this book will be equipped with definitive information on recent developments suitable for countering adversarial threats in AI/ML systems towards making them operate in a safe, reliable and seamless manner.
650 0 $a Computer security. $3 540555
650 0 $a Intelligent agents (Computer software) $x Security measures. $3 3489423
650 0 $a Artificial intelligence. $3 516317
650 0 $a Data protection. $3 590548
650 1 4 $a Artificial Intelligence. $3 769149
650 2 4 $a Security. $3 3134865
700 1 $a Dasgupta, Prithviraj. $3 3489421
700 1 $a Collins, Joseph B. $3 3489422
700 1 $a Mittu, Ranjeev. $3 2191219
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-55692-1
950 $a Computer Science (SpringerNature-11645)