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001 | 978-3-030-55692-1 | ||
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_a10.1007/978-3-030-55692-1 _2doi |
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_aAdversary-Aware Learning Techniques and Trends in Cybersecurity _h[electronic resource] / _cedited by Prithviraj Dasgupta, Joseph B. Collins, Ranjeev Mittu. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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300 |
_aX, 227 p. 68 illus., 50 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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505 | 0 | _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). | |
520 | _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. | ||
650 | 0 | _aArtificial intelligence. | |
650 | 0 | _aData protection. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aData and Information Security. |
700 | 1 |
_aDasgupta, Prithviraj. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aCollins, Joseph B. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aMittu, Ranjeev. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
|
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030556914 |
776 | 0 | 8 |
_iPrinted edition: _z9783030556938 |
776 | 0 | 8 |
_iPrinted edition: _z9783030556945 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-55692-1 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c177970 _d177970 |