Adversary-Aware Learning Techniques and Trends in Cybersecurity / edited by Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu.

Format:

Book

Contributor:

Dasgupta, Prithviraj., Editor.

Collins, Joseph B., Editor.

Mittu, Ranjeev, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence.

Data protection.

Artificial Intelligence.

Data and Information Security.

Local Subjects:

Artificial Intelligence.

Data and Information Security.

Physical Description:

1 online resource (X, 227 pages) : 68 illustrations, 50 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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.

Contents:

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).

Other Format:

Printed edition:

ISBN:

978-3-030-55692-1

9783030556921

Access Restriction:

Restricted for use by site license.