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Understanding AI in cybersecurity and secure AI = challenges, strategies and trends /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Understanding AI in cybersecurity and secure AI/ by Dilli Prasad Sharma ... [et al.].
Reminder of title:	challenges, strategies and trends /
other author:	Sharma, Dilli Prasad.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xiv, 250 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Part I: General -- Chapter 1: Why AI and Security? -- Chapter 2: Understanding AI and ML -- Part II: AI in Security -- Chapter 3: AI in Security -- Chapter 4: AI for Network Security -- Chapter 5: AI for Software Security -- Chapter 6: AI for Cloud Security -- Chapter 7: AI for IoT and OT Security -- Part III: Secure AI -- Chapter 8: AI Security and Privacy -- Chapter 9: Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI -- Chapter 10: General Framework for AI Security and Privacy -- Chapter 11: AI Safety and Fairness -- Chapter 12: AI Security Challenges, Opportunities and Future Work -- Chapter 13: Conclusion.
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence - Security measures. -
Online resource:	https://doi.org/10.1007/978-3-031-91524-6
ISBN:	9783031915246

Understanding AI in cybersecurity and secure AI = challenges, strategies and trends /
Understanding AI in cybersecurity and secure AIchallenges, strategies and trends /[electronic resource] :by Dilli Prasad Sharma ... [et al.]. - Cham :Springer Nature Switzerland :2025. - xiv, 250 p. :ill. (some col.), digital ;24 cm. - Progress in IS,2196-8713. - Progress in IS..

Part I: General -- Chapter 1: Why AI and Security? -- Chapter 2: Understanding AI and ML -- Part II: AI in Security -- Chapter 3: AI in Security -- Chapter 4: AI for Network Security -- Chapter 5: AI for Software Security -- Chapter 6: AI for Cloud Security -- Chapter 7: AI for IoT and OT Security -- Part III: Secure AI -- Chapter 8: AI Security and Privacy -- Chapter 9: Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI -- Chapter 10: General Framework for AI Security and Privacy -- Chapter 11: AI Safety and Fairness -- Chapter 12: AI Security Challenges, Opportunities and Future Work -- Chapter 13: Conclusion.

This book presents an overview of the emerging topics in Artificial Intelligence (AI) and cybersecurity and addresses the latest AI models that could be potentially applied to a range of cybersecurity areas. Furthermore, it provides different techniques of how to make the AI algorithms secure from adversarial attacks. The book presents the cyber threat landscape and explains the various spectrums of AI and the applications and limitations of AI in cybersecurity. Moreover, it explores the applications and limitations of secure AI. The authors discuss the three categories of machine learning (ML) models and reviews cutting-edge recent Deep Learning (DL) models. Furthermore, the book provides a general AI framework in security as well as different modules of the framework; similarly, chapter four proposes a general framework for secure AI. It explains different aspects of network security including malware and attacks. The book also includes a comprehensive study of various scopes of application security; categorised into three groups of smartphone, web application, and desktop application and delves into the concepts of cloud security. The authors discuss state-of-the-art Internet of Things (IoT) security and describe various challenges of AI for cybersecurity, such as data diversity, model customising, explainability, and time complexity and includes some future work. They provide a comprehensive understanding of adversarial machine learning including the up-to-date adversarial attacks and defences. The book finishes off with a discussion of the challenges and future work in secure AI. Overall, this book covers applications of AI models to various fields of cybersecurity and appeals not only to an scholarly audience but also to professionals wanting to learn more about the new developments in these areas.

ISBN: 9783031915246

Standard No.: 10.1007/978-3-031-91524-6doiSubjects--Topical Terms:

3595924
Artificial intelligence
--Security measures.

LC Class. No.: Q335

Dewey Class. No.: 006.3

Understanding AI in cybersecurity and secure AI = challenges, strategies and trends /
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505 0 $a Part I: General -- Chapter 1: Why AI and Security? -- Chapter 2: Understanding AI and ML -- Part II: AI in Security -- Chapter 3: AI in Security -- Chapter 4: AI for Network Security -- Chapter 5: AI for Software Security -- Chapter 6: AI for Cloud Security -- Chapter 7: AI for IoT and OT Security -- Part III: Secure AI -- Chapter 8: AI Security and Privacy -- Chapter 9: Defense Methods for Adversarial Attacks and Privacy Issues in Secure AI -- Chapter 10: General Framework for AI Security and Privacy -- Chapter 11: AI Safety and Fairness -- Chapter 12: AI Security Challenges, Opportunities and Future Work -- Chapter 13: Conclusion.
520 $a This book presents an overview of the emerging topics in Artificial Intelligence (AI) and cybersecurity and addresses the latest AI models that could be potentially applied to a range of cybersecurity areas. Furthermore, it provides different techniques of how to make the AI algorithms secure from adversarial attacks. The book presents the cyber threat landscape and explains the various spectrums of AI and the applications and limitations of AI in cybersecurity. Moreover, it explores the applications and limitations of secure AI. The authors discuss the three categories of machine learning (ML) models and reviews cutting-edge recent Deep Learning (DL) models. Furthermore, the book provides a general AI framework in security as well as different modules of the framework; similarly, chapter four proposes a general framework for secure AI. It explains different aspects of network security including malware and attacks. The book also includes a comprehensive study of various scopes of application security; categorised into three groups of smartphone, web application, and desktop application and delves into the concepts of cloud security. The authors discuss state-of-the-art Internet of Things (IoT) security and describe various challenges of AI for cybersecurity, such as data diversity, model customising, explainability, and time complexity and includes some future work. They provide a comprehensive understanding of adversarial machine learning including the up-to-date adversarial attacks and defences. The book finishes off with a discussion of the challenges and future work in secure AI. Overall, this book covers applications of AI models to various fields of cybersecurity and appeals not only to an scholarly audience but also to professionals wanting to learn more about the new developments in these areas.
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