03838nam 2200529 450 99646440050331620231120054110.097830305569213-030-55692-110.1007/978-3-030-55692-1(CKB)4100000011728410(DE-He213)978-3-030-55692-1(MiAaPQ)EBC6462069(PPN)25325549X(EXLCZ)99410000001172841020210312d2021 uy 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierAdversary-aware learning techniques and trends in cybersecurity /Prithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu1st ed. 2021.Cham, Switzerland :Springer,[2021]©20211 online resource (X, 227 p. 68 illus., 50 illus. in color.)Includes index.3-030-55691-3 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.Intelligent agents (Computer software)Security measuresArtificial intelligenceComputer securityIntelligent agents (Computer software)Security measures.Artificial intelligence.Computer security.016.391Collins Joseph B.Mittu RanjeevDasgupta PrithvirajMiAaPQMiAaPQMiAaPQBOOK996464400503316Adversary-aware learning techniques and trends in cybersecurity2814882UNISA