Generative Cybersecurity by Sikos Leslie (17 results)

- Softcover
Seller: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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- Softcover
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Paperback or Softback. Condition: New. Generative AI in Cybersecurity. Book.

- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting.…

- Softcover
Seller: preigu, Osnabrück, Germanypreigu
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Taschenbuch. Condition: Neu. Generative AI in Cybersecurity | Leslie F. Sikos | Taschenbuch | SpringerBriefs in Cybersecurity | x | Englisch | 2025 | Springer | EAN 9783032052490 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

- Softcover
- Print on Demand
Seller: Brook Bookstore On Demand, Napoli, NA, ItalyBrook Bookstore On Demand
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- Softcover
- Print on Demand
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting. 71 pp. Englisch.…

- Softcover
- Print on Demand
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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- Softcover
- Print on Demand
Seller: Biblios, frankfurt am main, HESSE, GermanyBiblios
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- Softcover
- Print on Demand
Seller: moluna, Greven, Germanymoluna
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

- Softcover
- Print on Demand
Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

- Softcover
- Print on Demand
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germanybuchversandmimpf2000
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores the most common generative AI (GenAI) tools and techniques used by malicious actors for hacking and cyber-deception, along with the security risks of large language models (LLMs). It also covers how LLM deployment and use can be secured, and how generative AI can be utilized in SOC automation.The rapid advancements and growing variety of publicly available generative AI tools enables cybersecurity use cases for threat modeling, security awareness support, web application scanning, actionable insights, and alert fatigue prevention. However, they also came with a steep rise in the number of offensive/rogue/malicious generative AI applications. With large language models, social engineering tactics can reach new heights in the efficiency of phishing campaigns and cyber-deception via synthetic media generation (misleading deepfake images and videos, faceswapping, morphs, and voice clones). The result is a new era of cybersecurity that necessitates innovative approaches to detect and mitigate sophisticated cyberattacks, and to prevent hyper-realistic cyber-deception.This work provides a starting point for researchers and students diving into malicious chatbot use, system administrators trying to harden the security of GenAI deployments, and organizations prone to sensitive data leak through shadow AI. It also benefits SOC analysts considering generative AI for partially automating incident detection and response, and GenAI vendors working on security guardrails against malicious prompting.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 84 pp. Englisch.…