LEADER 03838nam 2200529 450 001 996464400503316 005 20231120054110.0 010 $a9783030556921 010 $a3-030-55692-1 024 7 $a10.1007/978-3-030-55692-1 035 $a(CKB)4100000011728410 035 $a(DE-He213)978-3-030-55692-1 035 $a(MiAaPQ)EBC6462069 035 $a(PPN)25325549X 035 $a(EXLCZ)994100000011728410 100 $a20210312d2021 uy 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdversary-aware learning techniques and trends in cybersecurity /$fPrithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu 205 $a1st ed. 2021. 210 1$aCham, Switzerland :$cSpringer,$d[2021] 210 4$dİ2021 215 $a1 online resource (X, 227 p. 68 illus., 50 illus. in color.) 300 $aIncludes index. 311 $a3-030-55691-3 327 $aPart 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). 330 $aThis 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. 606 $aIntelligent agents (Computer software)$xSecurity measures 606 $aArtificial intelligence 606 $aComputer security 615 0$aIntelligent agents (Computer software)$xSecurity measures. 615 0$aArtificial intelligence. 615 0$aComputer security. 676 $a016.391 702 $aCollins$b Joseph B. 702 $aMittu$b Ranjeev 702 $aDasgupta$b Prithviraj 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996464400503316 996 $aAdversary-aware learning techniques and trends in cybersecurity$92814882 997 $aUNISA