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Adversary-aware learning techniques and trends in cybersecurity / / Prithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu
| Adversary-aware learning techniques and trends in cybersecurity / / Prithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (X, 227 p. 68 illus., 50 illus. in color.) |
| Disciplina | 016.391 |
| Soggetto topico |
Intelligent agents (Computer software) - Security measures
Artificial intelligence Computer security |
| ISBN |
9783030556921
3-030-55692-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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). |
| Record Nr. | UNINA-9910484456103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Adversary-aware learning techniques and trends in cybersecurity / / Prithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu
| Adversary-aware learning techniques and trends in cybersecurity / / Prithviraj Dasgupta; Joseph B Collins; Ranjeev Mittu |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (X, 227 p. 68 illus., 50 illus. in color.) |
| Disciplina | 016.391 |
| Soggetto topico |
Intelligent agents (Computer software) - Security measures
Artificial intelligence Computer security |
| ISBN |
9783030556921
3-030-55692-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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). |
| Record Nr. | UNISA-996464400503316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||